Circulating RNA signatures specific to preeclampsia

ABSTRACT

The present invention includes methods and materials for use in the detection preeclampsia and/or determining an increased risk for preeclampsia in a pregnant female, the method including identifying in a biosample obtained from the pregnant women a plurality of circulating RNA (C-RNA) molecules.

CONTINUING APPLICATION DATA

This application claims the benefit of U.S. Provisional Application Ser.No. 62/939,324, filed Nov. 22, 2019, which is incorporated by referenceherein.

FIELD OF INVENTION

The present invention relates generally to methods and materials for usein the detection and early risk assessment for the pregnancycomplication preeclampsia.

BACKGROUND

Preeclampsia is a condition that occurs only during pregnancy, affecting5% to 8% of all pregnancies. It is the direct cause of 10%-15% ofmaternal deaths and 40% of fetal deaths. The three main symptoms ofpreeclampsia may include high blood pressure, swelling of hands andfeet, and excess protein in the urine (proteinuria), occurring afterweek 20 of pregnancy. Other signs and symptoms of preeclampsia mayinclude severe headaches, changes in vision (including temporary loss ofvision, blurred vision or light sensitivity), nausea or vomiting,decreased urine output, decreased platelets levels (thrombocytopenia),impaired liver function, and shortness of breath, caused by fluid in thelung.

The more severe the preeclampsia and the earlier it occurs in pregnancy,the greater the risks for mother and baby. Preeclampsia may requireinduced labor and delivery or delivery by cesarean delivery. Leftuntreated, preeclampsia can lead to serious, even fatal, complicationsfor both the mother and baby. Complications of preeclampsia includefetal growth restriction, low birth weight, preterm birth, placentalabruption, HELLP syndrome (hemolysis, elevated liver enzymes, and lowplatelet count syndrome), eclampsia (a severe form of preeclampsia thatleads to seizures), organ damage, including kidney, liver, lung, heart,or eye damage, stroke or other brain injury. See, for example,“Preeclampsia—Symptoms and causes—Mayo Clinic,” Apr. 3, 2018, availableat on the worldwide web atmayoclinic.org/diseases-conditions/preeclampsia/symptoms-causes/syc-20355745.

With early detection and treatment, most women can deliver a healthybaby if preeclampsia is detected early and treated with regular prenatalcare. Although various protein biomarkers display changed levels inmaternal serum at presymptomatic stages, these biomarkers lackdiscriminative and predictive power in individual patients (Karumanchiand Granger, 2016, Hypertension; 67(2): 238-242). Thus, theidentification of biomarkers for the early detection of preeclampsia iscritical for the early diagnosis and treatment of preeclampsia.

SUMMARY OF THE INVENTION

The present invention includes a method of detecting preeclampsia and/ordetermining an increased risk for preeclampsia in a pregnant female, themethod including:

identifying in a biosample obtained from the pregnant women a pluralityof circulating RNA (C-RNA) molecules;

wherein a plurality of C-RNA molecules is selected from:

-   -   (a) a plurality of C-RNA molecules encoding at least a portion        of a protein selected from any one or more, any two or more, any        three or more, any four or more, any five or more, any six or        more, any seven or more, any eight or more, any nine or more,        any ten or more, any eleven or more, any twelve, any thirteen or        more, any fourteen or more, any fifteen or more, any sixteen or        more, any seventeen or more, any eighteen or more, any nineteen        or more, any twenty or more, any twenty one or more, any twenty        two or more, any twenty three or more, any twenty four or more,        any twenty five or more, up to all seventy-five of ARRDC2, JUN,        SKIL, ATP13A3, PDE8B, GSTA3, PAPPA2, TIPARP, LEP, RGP1, USP54,        CLEC4C, MRPS35, ARHGEF25, CUX2, HEATR9, FSTL3, DDI2, ZMYM6,        ST6GALNAC3, GBP2, NES, ETV3, ADAM17, ATOH8, SLC4A3, TRAF3IP1,        TTC21A, HEG1, ASTE1, TMEM108, ENC1, SCAMP1, ARRDC3, SLC26A2,        SLIT3, CLIC5, TNFRSF21, PPP1R17, TPST1, GATSL2, SPDYE5, HIPK2,        MTRNR2L6, CLCN1, GINS4, CRH, C10orf2, TRUB1, PRG2, ACY3, FAR2,        CD63, CKAP4, TPCN1, RNF6, THTPA, FOS, PARN, ORAI3, ELMO3, SMPD3,        SERPINF1, TMEM11, PSMD11, EBI3, CLEC4M, CCDC151, CPAMD8, CNFN,        LILRA4, ADA, C22orf39, PI4KAP1, and ARFGAP3; or    -   (b) a plurality of C-RNA molecules encoding at least a portion        of a protein selected from any one or more, any two or more, any        three or more, any four or more, any five or more, any six or        more, any seven or more, any eight or more, any nine or more,        any ten or more, any eleven or more, any twelve or more, any        thirteen or more, any fourteen or more, any fifteen or more, any        sixteen or more, any seventeen or more, any eighteen or more,        any nineteen or more, any twenty or more, any twenty one or        more, any twenty two or more, any twenty three or more, any        twenty four or more, any twenty five or more, any twenty six of        more, or all twenty-seven of TIMP4, FLG, HTRA4, AMPH, LCN6, CRH,        TEAD4, ARMS2, PAPPA2, SEMA3G, ADAMTS1, ALOX15B, SLC9A3R2, TIMP3,        IGFBP5, HSPA12B, CLEC4C, KRT5, PRG2, PRX, ARHGEF25, ADAMTS2,        DAAM2, FAM107A, LEP, NES, and VSIG4; or    -   (c) a plurality of C-RNA molecules encoding at least a portion        of a protein selected from any one or more, any two or more, any        three or more, any four or more, any five or more, any six or        more, any seven or more, any eight or more, any nine or more,        any ten or more, any eleven or more, any twelve, any thirteen or        more, any fourteen or more, any fifteen or more, any sixteen or        more, any seventeen or more, any eighteen or more, any nineteen        or more, any twenty or more, any twenty one or more, any twenty        two or more, any twenty three or more, any twenty four or more,        any twenty five or more, up to all one hundred twenty-two of        CYP26B1, IRF6, MYH14, PODXL, PPP1R3C, SH3RF2, TMC7, ZNF366,        ADCY1, C6, FAM219A, HAO2, IGIP, IL1R2, NTRK2, SH3PXD2A, SSUH2,        SULT2A1, FMO3, FSTL3, GATA5, HTRA1, C8B, H19, MN1, NFE2L1,        PRDM16, AP3B2, EMP1, FLNC, STAG3, CPB2, TENC1, RP1L1, A1CF,        NPR1, TEK, ERRFI1, ARHGEF15, CD34, RSPO3, ALPK3, SAMD4A,        ZCCHC24, LEAP2, MYL2, NRG3, ZBTB16, SERPINA3, AQP7, SRPX, UACA,        ANO1, FKBP5, SCN5A, PTPN21, CACNA1C, ERG, SOX17, WWTR1, AIF1L,        CA3, HRG, TAT, AQP7P1, ADRA2C, SYNPO, FN1, GPR116, KRT17, AZGP1,        BCL6B, KIF1C, CLIC5, GPR4, GJA5, OLAH, C14orf37, ZEB1, JAG2,        KIF26A, APOLD1, PNMT, MYOM3, PITPNM3, TIMP4, HTRA4, AMPH, LCN6,        CRH, TEAD4, ARMS2, PAPPA2, SEMA3G, ADAMTS1, ALOX15B, SLC9A3R2,        TIMP3, IGFBP5, HSPA12B, PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2,        FAM107A, LEP, NES, VSIG4, HBG2, CADM2, LAMPS, PTGDR2, NOMO1,        NXF3, PLD4, BPIFB3, PACSIN1, CUX2, FLG, CLEC4C, and KRT5; or    -   (d) a plurality of C-RNA molecules encoding at least a portion        of a protein selected from any one or more, any two or more, any        three or more, any four or more, any five or more, any six or        more, any seven or more, any eight or more, any nine or more,        any ten or more, any eleven or more, any twelve or more, any        thirteen or more, any fourteen or more, any fifteen or more, any        sixteen or more, any seventeen or more, any eighteen or more,        any nineteen or more, any twenty or more, any twenty-one or        more, any twenty-two or more, any twenty-three or more, any        twenty-four or more, any twenty-five or more, any twenty-six or        more, any twenty-seven or more, any twenty-eight or more, any        twenty-nine or more, or all thirty of VSIG4, ADAMTS2, NES,        FAM107A, LEP, DAAM2, ARHGEF25, TIMP3, PRX, ALOX15B, HSPA12B,        IGFBP5, CLEC4C, SLC9A3R2, ADAMTS1, SEMA3G, KRT5, AMPH, PRG2,        PAPPA2, TEAD4, CRH, PITPNM3, TIMP4, PNMT, ZEB1, APOLD1, PLD4,        CUX2, and HTRA4; or    -   (e) a plurality of C-RNA molecules encoding at least a portion        of a protein selected from any one or more, any two or more, any        three or more, any four or more, any five or more, any six or        more, any seven or more, any eight or more, any nine or more,        any ten or more, any eleven or more, any twelve or more, any        thirteen or more, any fourteen or more, any fifteen or more, any        sixteen or more, any seventeen or more, any eighteen or more,        any nineteen or more, any twenty or more, any twenty-one or        more, any twenty-two or more, any twenty-three or more, any        twenty-four or more, any twenty-five or more, or all twenty-six        of ADAMTS1, ADAMTS2, ALOX15B, AMPH, ARHGEF25, CELF4, DAAM2,        FAM107A, HSPA12B, HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26,        NES, OLAH, PACSIN1, PAPPA2, PRX, PTGDR2, SEMA3G, SLC9A3R2,        TIMP3, and VSIG4; or    -   (f) a plurality of C-RNA molecules encoding at least a portion        of a protein selected from any one or more, any two or more, any        three or more, any four or more, any five or more, any six or        more, any seven or more, any eight or more, any nine or more,        any ten or more, any eleven or more, any twelve or more, any        thirteen or more, any fourteen or more, any fifteen or more, any        sixteen or more, any seventeen or more, any eighteen or more,        any nineteen or more, any twenty or more, any twenty-one or        more, or all twenty-two of ADAMTS1, ADAMTS2, ALOX15B, ARHGEF25,        CELF4, DAAM2, FAM107A, HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP,        LRRC26, NES, OLAH, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and        VSIG4; or    -   (g) a plurality of C-RNA molecules encoding at least a portion        of a protein selected from any one or more, any two or more, any        three or more, any four or more, any five or more, any six or        more, any seven or more, any eight or more, any nine or more,        any ten or more, or all eleven of CLEC4C, ARHGEF25, ADAMTS2,        LEP, ARRDC2, SKIL, PAPPA2, VSIG4, ARRDC4, CRH, and NES        (including in some embodiments, the seven of ADAMTS2, ARHGEF25,        ARRDC2, CLEC4C, LEP, PAPPA2, and VSIG4; the eight of ADAMTS2,        ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, SKIL, and VSIG4; the        eight of ADAMTS2, ARHGEF25, ARRDC4, CLEC4C, LEP, NES, SKIL, and        VSIG4; the ten of ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C,        CRH, LEP, PAPPA2, SKIL, and VSIG4; the of six of ADAMTS2,        ARHGEF25, ARRDC2, CLEC4C, LEP, and SKIL; or the eight of        ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, LEP, PAPPA2, and        SKIL); or    -   (h) a plurality of C-RNA molecules encoding at least a portion        of a protein selected from any one or more, any two or more, any        three or more, any four or more, any five or more, any six or        more, any seven or more, any eight or more, any nine or more,        any ten or more, any eleven or more, any twelve or more, any        thirteen or more, any fourteen or more, any fifteen or more, any        sixteen or more, any seventeen or more, any eighteen or more,        any nineteen or more, any twenty or more, any twenty-one or        more, any twenty-two or more, any twenty-three or more, or all        twenty-four of LEP, PAPPA2, KCNA5, ADAMTS2, MYOM3, ATP13A3,        ARHGEF25, ADA, HTRA4, NES, CRH, ACY3, PLD4, SCT, NOX4, PACSIN1,        SERPINF1, SKIL, SEMA3G, TIPARP, LRRC26, PHEX, LILRA4, and PER1;        or    -   (i) a plurality of C-RNA molecules encoding at least a portion        of a protein selected from any one or more, any two or more, any        three or more, any four or more, any five or more, any six or        more, any seven or more, any eight or more, any nine or more,        any ten or more, any eleven or more, any twelve, any thirteen or        more, any fourteen or more, any fifteen or more, any sixteen or        more, any seventeen or more, any eighteen or more, any nineteen        or more, any twenty or more, any twenty one or more, any twenty        two or more, any twenty three or more, any twenty four or more,        any twenty five or more, any twenty-six or more, any        twenty-seven or more, any twenty-eight or more, any twenty-nine        or more, any thirty or more, any thirty-one or more, any        thirty-two or more, any thirty-three or more, any thirty-four or        more, any thirty-five or more, any thirty-six or more, any        thirty-seven or more, any thirty-eight or more, any thirty-nine        or more, any forty or more, any forty-one or more, any forty-two        or more, any forty-three or more, any forty-four or more, any        forty-five or more, any forty-six or more, any forty-seven or        more, any forty-eight or more, or all forth-nine of those listed        in Table S9 of Example 7; or    -   (j) a plurality of C-RNA molecules encoding at least a portion        of a protein selected from any one or more, any two or more, any        three or more, any four or more, any five or more, any six or        more, any seven or more, any eight or more, any nine or more,        any ten or more, any eleven or more, any twelve or more, or all        thirteen of AKAP2, ARRB1, CPSF7, INO80C, JAG1, MSMP, NR4A2,        PLEK, RAP1GAP2, SPEG, TRPS1, UBE2Q1, and ZNF768 is indicative of        preeclampsia and/or an increased risk for preeclampsia in the        pregnant women.

The present invention includes a method of detecting preeclampsia and/ordetermining an increased risk for preeclampsia in a pregnant female, themethod including:

obtaining a biosample from the pregnant female;

purifying a population of circulating RNA (C-RNA) molecules from thebiosample;

identifying protein coding sequences encoded by the C-RNA moleculeswithin the purified population of C-RNA molecules;

wherein protein coding sequences encoded by the C-RNA molecules encodingat least a portion of a protein is selected from:

-   -   (a) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve, any thirteen or more, any fourteen        or more, any fifteen or more, any sixteen or more, any seventeen        or more, any eighteen or more, any nineteen or more, any twenty        or more, any twenty one or more, any twenty two or more, any        twenty three or more, any twenty four or more, any twenty five        or more, any fifty or more, any seventy or more, or all        seventy-five of ARRDC2, JUN, SKIL, ATP13A3, PDE8B, GSTA3,        PAPPA2, TIPARP, LEP, RGP1, USP54, CLEC4C, MRPS35, ARHGEF25,        CUX2, HEATR9, FSTL3, DDI2, ZMYM6, ST6GALNAC3, GBP2, NES, ETV3,        ADAM17, ATOH8, SLC4A3, TRAF3IP1, TTC21A, HEG1, ASTE1, TMEM108,        ENC1, SCAMP1, ARRDC3, SLC26A2, SLIT3, CLIC5, TNFRSF21, PPP1R17,        TPST1, GATSL2, SPDYE5, HIPK2, MTRNR2L6, CLCN1, GINS4, CRH,        C10orf2, TRUB1, PRG2, ACY3, FAR2, CD63, CKAP4, TPCN1, RNF6,        THTPA, FOS, PARN, ORAI3, ELMO3, SMPD3, SERPINF1, TMEM11, PSMD11,        EBI3, CLEC4M, CCDC151, CPAMD8, CNFN, LILRA4, ADA, C22orf39,        PI4KAP1, and ARFGAP3; or    -   (b) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve or more, any thirteen or more, any        fourteen or more, any fifteen or more, any sixteen or more, any        seventeen or more, any eighteen or more, any nineteen or more,        any twenty or more, any twenty one or more, any twenty two or        more, any twenty three or more, any twenty four or more, any        twenty five or more, any twenty six of more, or all twenty-seven        of TIMP4, FLG, HTRA4, AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2,        SEMA3G, ADAMTS1, ALOX15B, SLC9A3R2, TIMP3, IGFBP5, HSPA12B,        CLEC4C, KRT5, PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2, FAM107A, LEP,        NES, and VSIG4; or    -   (c) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve, any thirteen or more, any fourteen        or more, any fifteen or more, any sixteen or more, any seventeen        or more, any eighteen or more, any nineteen or more, any twenty        or more, any twenty one or more, any twenty two or more, any        twenty three or more, any twenty four or more, any twenty five        or more, any fifty or more, any seventy-five or more, any one        hundred or more, or all one hundred twenty-two of CYP26B1, IRF6,        MYH14, PODXL, PPP1R3C, SH3RF2, TMC7, ZNF366, ADCY1, C6, FAM219A,        HAO2, IGIP, IL1R2, NTRK2, SH3PXD2A, SSUH2, SULT2A1, FMO3, FSTL3,        GATA5, HTRA1, C8B, H19, MN1, NFE2L1, PRDM16, AP3B2, EMP1, FLNC,        STAG3, CPB2, TENC1, RP1L1, A1CF, NPR1, TEK, ERRFI1, ARHGEF15,        CD34, RSPO3, ALPK3, SAMD4A, ZCCHC24, LEAP2, MYL2, NRG3, ZBTB16,        SERPINA3, AQP7, SRPX, UACA, ANO1, FKBP5, SCN5A, PTPN21, CACNA1C,        ERG, SOX17, WWTR1, AIF1L, CA3, HRG, TAT, AQP7P1, ADRA2C, SYNPO,        FN1, GPR116, KRT17, AZGP1, BCL6B, KIF1C, CLIC5, GPR4, GJA5,        OLAH, C14orf37, ZEB1, JAG2, KIF26A, APOLD1, PNMT, MYOM3,        PITPNM3, TIMP4, HTRA4, AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2,        SEMA3G, ADAMTS1, ALOX15B, SLC9A3R2, TIMP3, IGFBP5, HSPA12B,        PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2, FAM107A, LEP, NES, VSIG4,        HBG2, CADM2, LAMPS, PTGDR2, NOMO1, NXF3, PLD4, BPIFB3, PACSIN1,        CUX2, FLG, CLEC4C, and KRT5; or    -   (d) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve or more, any thirteen or more, any        fourteen or more, any fifteen or more, any sixteen or more, any        seventeen or more, any eighteen or more, any nineteen or more,        any twenty or more, any twenty-one or more, any twenty-two or        more, any twenty-three or more, any twenty-four or more, any        twenty-five or more, any twenty-six or more, any twenty-seven or        more, any twenty-eight or more, any twenty-nine or more, or all        thirty of VSIG4, ADAMTS2, NES, FAM107A, LEP, DAAM2, ARHGEF25,        TIMP3, PRX, ALOX15B, HSPA12B, IGFBP5, CLEC4C, SLC9A3R2, ADAMTS1,        SEMA3G, KRT5, AMPH, PRG2, PAPPA2, TEAD4, CRH, PITPNM3, TIMP4,        PNMT, ZEB1, APOLD1, PLD4, CUX2, and HTRA4; or    -   (e) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve or more, any thirteen or more, any        fourteen or more, any fifteen or more, any sixteen or more, any        seventeen or more, any eighteen or more, any nineteen or more,        any twenty or more, any twenty-one or more, any twenty-two or        more, any twenty-three or more, any twenty-four or more, any        twenty-five or more, or all twenty-six of ADAMTS1, ADAMTS2,        ALOX15B, AMPH, ARHGEF25, CELF4, DAAM2, FAM107A, HSPA12B, HTRA4,        IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH, PACSIN1,        PAPPA2, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and VSIG4; or    -   (f) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve or more, any thirteen or more, any        fourteen or more, any fifteen or more, any sixteen or more, any        seventeen or more, any eighteen or more, any nineteen or more,        any twenty or more, any twenty-one or more, or all twenty-two of        ADAMTS1, ADAMTS2, ALOX15B, ARHGEF25, CELF4, DAAM2, FAM107A,        HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH, PRX,        PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and VSIG4; or    -   (g) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, or        all eleven of CLEC4C, ARHGEF25, ADAMTS2, LEP, ARRDC2, SKIL,        PAPPA2, VSIG4, ARRDC4, CRH, and NES (including in some        embodiments, the seven of ADAMTS2, ARHGEF25, ARRDC2, CLEC4C,        LEP, PAPPA2, and VSIG4; the eight of ADAMTS2, ARHGEF25, ARRDC2,        CLEC4C, LEP, PAPPA2, SKIL, and VSIG4; the eight of ADAMTS2,        ARHGEF25, ARRDC4, CLEC4C, LEP, NES, SKIL, and VSIG4; the ten of        ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, CRH, LEP, PAPPA2,        SKIL, and VSIG4; the of six of ADAMTS2, ARHGEF25, ARRDC2,        CLEC4C, LEP, and SKIL; or the eight of ADAMTS2, ARHGEF25,        ARRDC2, ARRDC4, CLEC4C, LEP, PAPPA2, and SKIL); or    -   (h) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve or more, any thirteen or more, any        fourteen or more, any fifteen or more, any sixteen or more, any        seventeen or more, any eighteen or more, any nineteen or more,        any twenty or more, any twenty-one or more, any twenty-two or        more, any twenty-three or more, or all twenty-four of LEP,        PAPPA2, KCNA5, ADAMTS2, MYOM3, ATP13A3, ARHGEF25, ADA, HTRA4,        NES, CRH, ACY3, PLD4, SCT, NOX4, PACSIN1, SERPINF1, SKIL,        SEMA3G, TIPARP, LRRC26, PHEX, LILRA4, and PER1; or    -   (i) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve, any thirteen or more, any fourteen        or more, any fifteen or more, any sixteen or more, any seventeen        or more, any eighteen or more, any nineteen or more, any twenty        or more, any twenty one or more, any twenty two or more, any        twenty three or more, any twenty four or more, any twenty five        or more, any twenty-six or more, any twenty-seven or more, any        twenty-eight or more, any twenty-nine or more, any thirty or        more, any thirty-one or more, any thirty-two or more, any        thirty-three or more, any thirty-four or more, any thirty-five        or more, any thirty-six or more, any thirty-seven or more, any        thirty-eight or more, any thirty-nine or more, any forty or        more, any forty-one or more, any forty-two or more, any        forty-three or more, any forty-four or more, any forty-five or        more, any forty-six or more, any forty-seven or more, any        forty-eight or more, or all forth-nine of those listed in Table        S9 of Example 7; or    -   (j) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve or more, or all thirteen of AKAP2,        ARRB1, CPSF7, INO80C, JAG1, MSMP, NR4A2, PLEK, RAP1GAP2, SPEG,        TRPS1, UBE2Q1, and ZNF768 is indicative of preeclampsia and/or        an increased risk for preeclampsia in the pregnant women.

In some aspects, identifying protein coding sequences encoded by C-RNAmolecules within the biosample includes hybridization, reversetranscriptase PCR, microarray chip analysis, or sequencing.

In some aspects, identifying protein coding sequences encoded by theC-RNA molecules within the biosample includes sequencing, including, forexample, massively parallel sequencing of clonally amplified moleculesand/or RNA sequencing.

In some aspects, the method further includes removing intact cells fromthe biosample; treating the biosample with a deoxynuclease (DNase) toremove cell free DNA (cfDNA); synthesizing complementary DNA (cDNA) fromC-RNA molecules in the biosample; and/or enriching the cDNA sequencesfor DNA sequences that encode proteins by exome enrichment prior toidentifying protein coding sequence encoded by the circulating RNA(C-RNA) molecules.

The present invention includes a method of detecting preeclampsia and/ordetermining an increased risk for preeclampsia in a pregnant female, themethod including:

obtaining a biological sample from the pregnant female;

removing intact cells from the biosample;

treating the biosample with a deoxynuclease (DNase) to remove cell freeDNA (cfDNA);

synthesizing complementary DNA (cDNA) from RNA molecules in thebiosample;

enriching the cDNA sequences for DNA sequences that encode proteins(exome enrichment);

sequencing the resulting enriched cDNA sequences; and

identifying protein coding sequences encoded by enriched C-RNAmolecules;

wherein protein coding sequences encoded by the C-RNA molecules encodingat least a portion of a protein selected from:

-   -   (a) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve, any thirteen or more, any fourteen        or more, any fifteen or more, any sixteen or more, any seventeen        or more, any eighteen or more, any nineteen or more, any twenty        or more, any twenty one or more, any twenty two or more, any        twenty three or more, any twenty four or more, any twenty five        or more, up to all seventy-five of ARRDC2, JUN, SKIL, ATP13A3,        PDE8B, GSTA3, PAPPA2, TIPARP, LEP, RGP1, USP54, CLEC4C, MRPS35,        ARHGEF25, CUX2, HEATR9, FSTL3, DDI2, ZMYM6, ST6GALNAC3, GBP2,        NES, ETV3, ADAM17, ATOH8, SLC4A3, TRAF3IP1, TTC21A, HEG1, ASTE1,        TMEM108, ENC1, SCAMP1, ARRDC3, SLC26A2, SLIT3, CLIC5, TNFRSF21,        PPP1R17, TPST1, GATSL2, SPDYE5, HIPK2, MTRNR2L6, CLCN1, GINS4,        CRH, C10orf2, TRUB1, PRG2, ACY3, FAR2, CD63, CKAP4, TPCN1, RNF6,        THTPA, FOS, PARN, ORAI3, ELMO3, SMPD3, SERPINF1, TMEM11, PSMD11,        EBI3, CLEC4M, CCDC151, CPAMD8, CNFN, LILRA4, ADA, C22orf39,        PI4KAP1, and ARFGAP3; or    -   (b) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve, any thirteen or more, any fourteen        or more, any fifteen or more, any sixteen or more, any seventeen        or more, any eighteen or more, any nineteen or more, any twenty        or more, any twenty one or more, any twenty two or more, any        twenty three or more, any twenty four or more, any twenty five        or more, any twenty six of more, or all twenty-seven of TIMP4,        FLG, HTRA4, AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2, SEMA3G,        ADAMTS1, ALOX15B, SLC9A3R2, TIMP3, IGFBP5, HSPA12B, CLEC4C,        KRT5, PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2, FAM107A, LEP, NES,        and VSIG4; or    -   (c) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve or more, any thirteen or more, any        fourteen or more, any fifteen or more, any sixteen or more, any        seventeen or more, any eighteen or more, any nineteen or more,        any twenty or more, any twenty one or more, any twenty two or        more, any twenty three or more, any twenty four or more, any        twenty five or more, up to all one hundred twenty-two of        CYP26B1, IRF6, MYH14, PODXL, PPP1R3C, SH3RF2, TMC7, ZNF366,        ADCY1, C6, FAM219A, HAO2, IGIP, IL1R2, NTRK2, SH3PXD2A, SSUH2,        SULT2A1, FMO3, FSTL3, GATA5, HTRA1, C8B, H19, MN1, NFE2L1,        PRDM16, AP3B2, EMP1, FLNC, STAG3, CPB2, TENC1, RP1L1, A1CF,        NPR1, TEK, ERRFI1, ARHGEF15, CD34, RSPO3, ALPK3, SAMD4A,        ZCCHC24, LEAP2, MYL2, NRG3, ZBTB16, SERPINA3, AQP7, SRPX, UACA,        ANO1, FKBP5, SCN5A, PTPN21, CACNA1C, ERG, SOX17, WWTR1, AIF1L,        CA3, HRG, TAT, AQP7P1, ADRA2C, SYNPO, FN1, GPR116, KRT17, AZGP1,        BCL6B, KIF1C, CLIC5, GPR4, GJA5, OLAH, C14orf37, ZEB1, JAG2,        KIF26A, APOLD1, PNMT, MYOM3, PITPNM3, TIMP4, HTRA4, AMPH, LCN6,        CRH, TEAD4, ARMS2, PAPPA2, SEMA3G, ADAMTS1, ALOX15B, SLC9A3R2,        TIMP3, IGFBP5, HSPA12B, PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2,        FAM107A, LEP, NES, VSIG4, HBG2, CADM2, LAMPS, PTGDR2, NOMO1,        NXF3, PLD4, BPIFB3, PACSIN1, CUX2, FLG, CLEC4C, and KRT5; or    -   (d) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve or more, any thirteen or more, any        fourteen or more, any fifteen or more, any sixteen or more, any        seventeen or more, any eighteen or more, any nineteen or more,        any twenty or more, any twenty-one or more, any twenty-two or        more, any twenty-three or more, any twenty-four or more, any        twenty-five or more, any twenty-six or more, any twenty-seven or        more, any twenty-eight or more, any twenty-nine or more, or all        thirty of VSIG4, ADAMTS2, NES, FAM107A, LEP, DAAM2, ARHGEF25,        TIMP3, PRX, ALOX15B, HSPA12B, IGFBP5, CLEC4C, SLC9A3R2, ADAMTS1,        SEMA3G, KRT5, AMPH, PRG2, PAPPA2, TEAD4, CRH, PITPNM3, TIMP4,        PNMT, ZEB1, APOLD1, PLD4, CUX2, and HTRA4; or    -   (e) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve or more, any thirteen or more, any        fourteen or more, any fifteen or more, any sixteen or more, any        seventeen or more, any eighteen or more, any nineteen or more,        any twenty or more, any twenty-one or more, any twenty-two or        more, any twenty-three or more, any twenty-four or more, any        twenty-five or more, or all twenty-six of ADAMTS1, ADAMTS2,        ALOX15B, AMPH, ARHGEF25, CELF4, DAAM2, FAM107A, HSPA12B, HTRA4,        IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH, PACSIN1,        PAPPA2, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and VSIG4; or    -   (f) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve or more, any thirteen or more, any        fourteen or more, any fifteen or more, any sixteen or more, any        seventeen or more, any eighteen or more, any nineteen or more,        any twenty or more, any twenty-one or more, or all twenty-two of        ADAMTS1, ADAMTS2, ALOX15B, ARHGEF25, CELF4, DAAM2, FAM107A,        HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH, PRX,        PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and VSIG4; or    -   (g) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, or        all eleven of CLEC4C, ARHGEF25, ADAMTS2, LEP, ARRDC2, SKIL,        PAPPA2, VSIG4, ARRDC4, CRH, and NES (including in some        embodiments, the seven of ADAMTS2, ARHGEF25, ARRDC2, CLEC4C,        LEP, PAPPA2, and VSIG4; the eight of ADAMTS2, ARHGEF25, ARRDC2,        CLEC4C, LEP, PAPPA2, SKIL, and VSIG4; the eight of ADAMTS2,        ARHGEF25, ARRDC4, CLEC4C, LEP, NES, SKIL, and VSIG4; the ten of        ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, CRH, LEP, PAPPA2,        SKIL, and VSIG4; the of six of ADAMTS2, ARHGEF25, ARRDC2,        CLEC4C, LEP, and SKIL; or the eight of ADAMTS2, ARHGEF25,        ARRDC2, ARRDC4, CLEC4C, LEP, PAPPA2, and SKIL); or    -   (h) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve or more, any thirteen or more, any        fourteen or more, any fifteen or more, any sixteen or more, any        seventeen or more, any eighteen or more, any nineteen or more,        any twenty or more, any twenty-one or more, any twenty-two or        more, any twenty-three or more, or all twenty-four of LEP,        PAPPA2, KCNA5, ADAMTS2, MYOM3, ATP13A3, ARHGEF25, ADA, HTRA4,        NES, CRH, ACY3, PLD4, SCT, NOX4, PACSIN1, SERPINF1, SKIL,        SEMA3G, TIPARP, LRRC26, PHEX, LILRA4, and PER1; or    -   (i) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve, any thirteen or more, any fourteen        or more, any fifteen or more, any sixteen or more, any seventeen        or more, any eighteen or more, any nineteen or more, any twenty        or more, any twenty one or more, any twenty two or more, any        twenty three or more, any twenty four or more, any twenty five        or more, any twenty-six or more, any twenty-seven or more, any        twenty-eight or more, any twenty-nine or more, any thirty or        more, any thirty-one or more, any thirty-two or more, any        thirty-three or more, any thirty-four or more, any thirty-five        or more, any thirty-six or more, any thirty-seven or more, any        thirty-eight or more, any thirty-nine or more, any forty or        more, any forty-one or more, any forty-two or more, any        forty-three or more, any forty-four or more, any forty-five or        more, any forty-six or more, any forty-seven or more, any        forty-eight or more, or all forth-nine of those listed in Table        S9 of Example 7; or    -   (j) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve or more, or all thirteen of AKAP2,        ARRB1, CPSF7, INO80C, JAG1, MSMP, NR4A2, PLEK, RAP1GAP2, SPEG,        TRPS1, UBE2Q1, and ZNF768 is indicative of preeclampsia and/or        an increased risk for preeclampsia in the pregnant women.

The present invention includes a method of identifying a circulating RNAsignature associated with an increased risk of preeclampsia, the methodincluding obtaining a biological sample from the pregnant female;removing intact cells from the biosample; treating the biosample with adeoxynuclease (DNase) to remove cell free DNA (cfDNA); synthesizingcomplementary DNA (cDNA) from RNA molecules in the biosample; enrichingthe cDNA sequences for DNA sequences that encode proteins (exomeenrichment); sequencing the resulting enriched cDNA sequences; andidentifying protein coding sequences encoded by enriched C-RNAmolecules.

The present invention includes a method including:

obtaining a biological sample from the pregnant female;

removing intact cells from the biosample;

treating the biosample with a deoxynuclease (DNase) to remove cell freeDNA (cfDNA);

synthesizing complementary DNA (cDNA) from RNA molecules in thebiosample;

enriching the cDNA sequences for DNA sequences that encode proteins(exome enrichment);

sequencing the resulting enriched cDNA sequences; and

identifying protein coding sequences encoded by the enriched C-RNAmolecules;

wherein the protein coding sequences include at least a portion of aprotein selected from:

-   -   (a) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve, any thirteen or more, any fourteen        or more, any fifteen or more, any sixteen or more, any seventeen        or more, any eighteen or more, any nineteen or more, any twenty        or more, any twenty one or more, any twenty two or more, any        twenty three or more, any twenty four or more, any twenty five        up to all seventy-five ARRDC2, JUN, SKIL, ATP13A3, PDE8B, GSTA3,        PAPPA2, TIPARP, LEP, RGP1, USP54, CLEC4C, MRPS35, ARHGEF25,        CUX2, HEATR9, FSTL3, DDI2, ZMYM6, ST6GALNAC3, GBP2, NES, ETV3,        ADAM17, ATOH8, SLC4A3, TRAF3IP1, TTC21A, HEG1, ASTE1, TMEM108,        ENC1, SCAMP1, ARRDC3, SLC26A2, SLIT3, CLIC5, TNFRSF21, PPP1R17,        TPST1, GATSL2, SPDYE5, HIPK2, MTRNR2L6, CLCN1, GINS4, CRH,        C10orf2, TRUB1, PRG2, ACY3, FAR2, CD63, CKAP4, TPCN1, RNF6,        THTPA, FOS, PARN, ORAI3, ELMO3, SMPD3, SERPINF1, TMEM11, PSMD11,        EBI3, CLEC4M, CCDC151, CPAMD8, CNFN, LILRA4, ADA, C22orf39,        PI4KAP1, and ARFGAP3; or    -   (b) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve or more, any thirteen or more, any        fourteen or more, any fifteen or more, any sixteen or more, any        seventeen or more, any eighteen or more, any nineteen or more,        any twenty or more, any twenty one or more, any twenty two or        more, any twenty three or more, any twenty four or more, any        twenty five or more, any twenty six of more, or all twenty-seven        of TIMP4, FLG, HTRA4, AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2,        SEMA3G, ADAMTS1, ALOX15B, SLC9A3R2, TIMP3, IGFBP5, HSPA12B,        CLEC4C, KRT5, PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2, FAM107A, LEP,        NES, and VSIG4; or    -   (c) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve, any thirteen or more, any fourteen        or more, any fifteen or more, any sixteen or more, any seventeen        or more, any eighteen or more, any nineteen or more, any twenty        or more, any twenty one or more, any twenty two or more, any        twenty three or more, any twenty four or more, any twenty five        or more, up to all one hundred twenty-two of CYP26B1, IRF6,        MYH14, PODXL, PPP1R3C, SH3RF2, TMC7, ZNF366, ADCY1, C6, FAM219A,        HAO2, IGIP, IL1R2, NTRK2, SH3PXD2A, SSUH2, SULT2A1, FMO3, FSTL3,        GATA5, HTRA1, C8B, H19, MN1, NFE2L1, PRDM16, AP3B2, EMP1, FLNC,        STAG3, CPB2, TENC1, RP1L1, A1CF, NPR1, TEK, ERRFI1, ARHGEF15,        CD34, RSPO3, ALPK3, SAMD4A, ZCCHC24, LEAP2, MYL2, NRG3, ZBTB16,        SERPINA3, AQP7, SRPX, UACA, ANO1, FKBP5, SCN5A, PTPN21, CACNA1C,        ERG, SOX17, WWTR1, AIF1L, CA3, HRG, TAT, AQP7P1, ADRA2C, SYNPO,        FN1, GPR116, KRT17, AZGP1, BCL6B, KIF1C, CLIC5, GPR4, GJA5,        OLAH, C14orf37, ZEB1, JAG2, KIF26A, APOLD1, PNMT, MYOM3,        PITPNM3, TIMP4, HTRA4, AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2,        SEMA3G, ADAMTS1, ALOX15B, SLC9A3R2, TIMP3, IGFBP5, HSPA12B,        PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2, FAM107A, LEP, NES, VSIG4,        HBG2, CADM2, LAMPS, PTGDR2, NOMO1, NXF3, PLD4, BPIFB3, PACSIN1,        CUX2, FLG, CLEC4C, and KRT5; or    -   (d) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve or more, any thirteen or more, any        fourteen or more, any fifteen or more, any sixteen or more, any        seventeen or more, any eighteen or more, any nineteen or more,        any twenty or more, any twenty-one or more, any twenty-two or        more, any twenty-three or more, any twenty-four or more, any        twenty-five or more, any twenty-six or more, any twenty-seven or        more, any twenty-eight or more, any twenty-nine or more, or all        thirty of VSIG4, ADAMTS2, NES, FAM107A, LEP, DAAM2, ARHGEF25,        TIMP3, PRX, ALOX15B, HSPA12B, IGFBP5, CLEC4C, SLC9A3R2, ADAMTS1,        SEMA3G, KRT5, AMPH, PRG2, PAPPA2, TEAD4, CRH, PITPNM3, TIMP4,        PNMT, ZEB1, APOLD1, PLD4, CUX2, and HTRA4; or    -   (e) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve or more, any thirteen or more, any        fourteen or more, any fifteen or more, any sixteen or more, any        seventeen or more, any eighteen or more, any nineteen or more,        any twenty or more, any twenty-one or more, any twenty-two or        more, any twenty-three or more, any twenty-four or more, any        twenty-five or more, or all twenty-six of ADAMTS1, ADAMTS2,        ALOX15B, AMPH, ARHGEF25, CELF4, DAAM2, FAM107A, HSPA12B, HTRA4,        IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH, PACSIN1,        PAPPA2, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and VSIG4; or    -   (f) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve or more, any thirteen or more, any        fourteen or more, any fifteen or more, any sixteen or more, any        seventeen or more, any eighteen or more, any nineteen or more,        any twenty or more, any twenty-one or more, or all twenty-two of        ADAMTS1, ADAMTS2, ALOX15B, ARHGEF25, CELF4, DAAM2, FAM107A,        HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH, PRX,        PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and VSIG4; or    -   (g) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, or        all eleven of CLEC4C, ARHGEF25, ADAMTS2, LEP, ARRDC2, SKIL,        PAPPA2, VSIG4, ARRDC4, CRH, and NES (including in some        embodiments, the seven of ADAMTS2, ARHGEF25, ARRDC2, CLEC4C,        LEP, PAPPA2, and VSIG4; the eight of ADAMTS2, ARHGEF25, ARRDC2,        CLEC4C, LEP, PAPPA2, SKIL, and VSIG4; the eight of ADAMTS2,        ARHGEF25, ARRDC4, CLEC4C, LEP, NES, SKIL, and VSIG4; the ten of        ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, CRH, LEP, PAPPA2,        SKIL, and VSIG4; the of six of ADAMTS2, ARHGEF25, ARRDC2,        CLEC4C, LEP, and SKIL; or the eight of ADAMTS2, ARHGEF25,        ARRDC2, ARRDC4, CLEC4C, LEP, PAPPA2, and SKIL); or    -   (h) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve or more, any thirteen or more, any        fourteen or more, any fifteen or more, any sixteen or more, any        seventeen or more, any eighteen or more, any nineteen or more,        any twenty or more, any twenty-one or more, any twenty-two or        more, any twenty-three or more, or all twenty-four of LEP,        PAPPA2, KCNA5, ADAMTS2, MYOM3, ATP13A3, ARHGEF25, ADA, HTRA4,        NES, CRH, ACY3, PLD4, SCT, NOX4, PACSIN1, SERPINF1, SKIL,        SEMA3G, TIPARP, LRRC26, PHEX, LILRA4, and PER1; or    -   (i) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve, any thirteen or more, any fourteen        or more, any fifteen or more, any sixteen or more, any seventeen        or more, any eighteen or more, any nineteen or more, any twenty        or more, any twenty one or more, any twenty two or more, any        twenty three or more, any twenty four or more, any twenty five        or more, any twenty-six or more, any twenty-seven or more, any        twenty-eight or more, any twenty-nine or more, any thirty or        more, any thirty-one or more, any thirty-two or more, any        thirty-three or more, any thirty-four or more, any thirty-five        or more, any thirty-six or more, any thirty-seven or more, any        thirty-eight or more, any thirty-nine or more, any forty or        more, any forty-one or more, any forty-two or more, any        forty-three or more, any forty-four or more, any forty-five or        more, any forty-six or more, any forty-seven or more, any        forty-eight or more, or all forth-nine of those listed in Table        S9 of Example 7; or    -   (j) any one or more, any two or more, any three or more, any        four or more, any five or more, any six or more, any seven or        more, any eight or more, any nine or more, any ten or more, any        eleven or more, any twelve or more, or all thirteen of AKAP2,        ARRB1, CPSF7, INO80C, JAG1, MSMP, NR4A2, PLEK, RAP1GAP2, SPEG,        TRPS1, UBE2Q1, and ZNF768.

In some aspects, the biosample includes plasma.

In some aspects, the biosample is obtained from a pregnant female atless than 16 weeks gestation or at less than 20 weeks gestation.

In some aspects, the biosample is obtained from a pregnant female atgreater than 20 weeks gestation.

The present invention includes a circulating RNA (C-RNA) signature foran elevated risk of preeclampsia, the C-RNA signature encoding at leasta portion of any one or more, any two or more, any three or more, anyfour or more, any five or more, any six or more, any seven or more, anyeight or more, any nine or more, any ten or more, any eleven or more,any twelve, any thirteen or more, any fourteen or more, any fifteen ormore, any sixteen or more, any seventeen or more, any eighteen or more,any nineteen or more, any twenty or more, any twenty one or more, anytwenty two or more, any twenty three or more, any twenty four or more,any twenty five or more, any fifty or more, any seventy or more, up toall seventy-five ARRDC2, JUN, SKIL, ATP13A3, PDE8B, GSTA3, PAPPA2,TIPARP, LEP, RGP1, USP54, CLEC4C, MRPS35, ARHGEF25, CUX2, HEATR9, FSTL3,DDI2, ZMYM6, ST6GALNAC3, GBP2, NES, ETV3, ADAM17, ATOH8, SLC4A3,TRAF3IP1, TTC21A, HEG1, ASTE1, TMEM108, ENC1, SCAMP1, ARRDC3, SLC26A2,SLIT3, CLIC5, TNFRSF21, PPP1R17, TPST1, GATSL2, SPDYE5, HIPK2, MTRNR2L6,CLCN1, GINS4, CRH, C10orf2, TRUB1, PRG2, ACY3, FAR2, CD63, CKAP4, TPCN1,RNF6, THTPA, FOS, PARN, ORAI3, ELMO3, SMPD3, SERPINF1, TMEM11, PSMD11,EBI3, CLEC4M, CCDC151, CPAMD8, CNFN, LILRA4, ADA, C22orf39, PI4KAP1, andARFGAP3.

The present invention includes a circulating RNA (C-RNA) signature foran elevated risk of preeclampsia, the C-RNA signature encoding at leasta portion of any one or more, any two or more, any three or more, anyfour or more, any five or more, any six or more, any seven or more, anyeight or more, any nine or more, any ten or more, any eleven or more,any twelve or more, any thirteen or more, any fourteen or more, anyfifteen or more, any sixteen or more, any seventeen or more, anyeighteen or more, any nineteen or more, any twenty or more, any twentyone or more, any twenty two or more, any twenty three or more, anytwenty four or more, any twenty five or more, any twenty six of more, orall twenty-seven of TIMP4, FLG, HTRA4, AMPH, LCN6, CRH, TEAD4, ARMS2,PAPPA2, SEMA3G, ADAMTS1, ALOX15B, SLC9A3R2, TIMP3, IGFBP5, HSPA12B,CLEC4C, KRT5, PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2, FAM107A, LEP, NES,and VSIG4.

The present invention includes a circulating RNA (C-RNA) signature foran elevated risk of preeclampsia, the C-RNA signature encoding a least aportion of a plurality of CYP26B1, IRF6, MYH14, PODXL, PPP1R3C, SH3RF2,TMC7, ZNF366, ADCY1, C6, FAM219A, HAO2, IGIP, IL1R2, NTRK2, SH3PXD2A,SSUH2, SULT2A1, FMO3, FSTL3, GATA5, HTRA1, C8B, H19, MN1, NFE2L1,PRDM16, AP3B2, EMP1, FLNC, STAG3, CPB2, TENC1, RP1L1, A1CF, NPR1, TEK,ERRF1, ARHGEF15, CD34, RSPO3, ALPK3, SAMD4A, ZCCHC24, LEAP2, MYL2, NRG3,ZBTB16, SERPINA3, AQP7, SRPX, UACA, ANO1, FKBP5, SCN5A, PTPN21, CACNA1C,ERG, SOX17, WWTR1, AIF1L, CA3, HRG, TAT, AQP7P1, ADRA2C, SYNPO, FN1,GPR116, KRT17, AZGP1, BCL6B, KIF1C, CLIC5, GPR4, GJA5, OLAH, C14orf37,ZEB1, JAG2, KIF26A, APOLD1, PNMT, MYOM3, PITPNM3, TIMP4, HTRA4, AMPH,LCN6, CRH, TEAD4, ARMS2, PAPPA2, SEMA3G, ADAMTS1, ALOX15B, SLC9A3R2,TIMP3, IGFBP5, HSPA12B, PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2, FAM107A,LEP, NES, VSIG4, HBG2, CADM2, LAMPS, PTGDR2, NOMO1, NXF3, PLD4, BPIFB3,PACSIN1, CUX2, FLG, CLEC4C, and KRT5.

The present invention includes a circulating RNA (C-RNA) signature foran elevated risk of preeclampsia, the C-RNA signature encoding at leasta portion of any one or more, any two or more, any three or more, anyfour or more, any five or more, any six or more, any seven or more, anyeight or more, any nine or more, any ten or more, any eleven or more,any twelve or more, any thirteen or more, any fourteen or more, anyfifteen or more, any sixteen or more, any seventeen or more, anyeighteen or more, any nineteen or more, any twenty or more, anytwenty-one or more, any twenty-two or more, any twenty-three or more,any twenty-four or more, any twenty-five or more, any twenty-six ormore, any twenty-seven or more, any twenty-eight or more, anytwenty-nine or more, or all thirty of VSIG4, ADAMTS2, NES, FAM107A, LEP,DAAM2, ARHGEF25, TIMP3, PRX, ALOX15B, HSPA12B, IGFBP5, CLEC4C, SLC9A3R2,ADAMTS1, SEMA3G, KRT5, AMPH, PRG2, PAPPA2, TEAD4, CRH, PITPNM3, TIMP4,PNMT, ZEB1, APOLD1, PLD4, CUX2, and HTRA4.

The present invention includes a circulating RNA (C-RNA) signature foran elevated risk of preeclampsia, the C-RNA signature encoding at leasta portion of any one or more, any two or more, any three or more, anyfour or more, any five or more, any six or more, any seven or more, anyeight or more, any nine or more, any ten or more, any eleven or more,any twelve or more, any thirteen or more, any fourteen or more, anyfifteen or more, any sixteen or more, any seventeen or more, anyeighteen or more, any nineteen or more, any twenty or more, anytwenty-one or more, any twenty-two or more, any twenty-three or more,any twenty-four or more, any twenty-five or more, or all twenty-six ofADAMTS1, ADAMTS2, ALOX15B, AMPH, ARHGEF25, CELF4, DAAM2, FAM107A,HSPA12B, HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH,PACSIN1, PAPPA2, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and VSIG4.

The present invention includes a circulating RNA (C-RNA) signature foran elevated risk of preeclampsia, the C-RNA signature encoding at leasta portion of any one or more, any two or more, any three or more, anyfour or more, any five or more, any six or more, any seven or more, anyeight or more, any nine or more, any ten or more, any eleven or more,any twelve or more, any thirteen or more, any fourteen or more, anyfifteen or more, any sixteen or more, any seventeen or more, anyeighteen or more, any nineteen or more, any twenty or more, anytwenty-one or more, or all twenty-two of ADAMTS1, ADAMTS2, ALOX15B,ARHGEF25, CELF4, DAAM2, FAM107A, HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP,LRRC26, NES, OLAH, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and VSIG4.

The present invention includes a circulating RNA (C-RNA) signature foran elevated risk of preeclampsia, the C-RNA signature encoding at leasta portion of any one or more, any two or more, any three or more, anyfour or more, any five or more, any six or more, any seven or more, anyeight or more, any nine or more, any ten or more, or all eleven ofCLEC4C, ARHGEF25, ADAMTS2, LEP, ARRDC2, SKIL, PAPPA2, VSIG4, ARRDC4,CRH, and NES, including in some embodiments, the seven of ADAMTS2,ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, and VSIG4; the eight of ADAMTS2,ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, SKIL, and VSIG4; the eight ofADAMTS2, ARHGEF25, ARRDC4, CLEC4C, LEP, NES, SKIL, and VSIG4; the ten ofADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, CRH, LEP, PAPPA2, SKIL, andVSIG4; the of six of ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, and SKIL;or the eight of ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, LEP, PAPPA2,and SKIL.

The present invention includes a circulating RNA (C-RNA) signature foran elevated risk of preeclampsia, the C-RNA signature encoding at leasta portion of any one or more, any two or more, any three or more, anyfour or more, any five or more, any six or more, any seven or more, anyeight or more, any nine or more, any ten or more, any eleven or more,any twelve or more, any thirteen or more, any fourteen or more, anyfifteen or more, any sixteen or more, any seventeen or more, anyeighteen or more, any nineteen or more, any twenty or more, anytwenty-one or more, any twenty-two or more, any twenty-three or more, orall twenty-four of LEP, PAPPA2, KCNA5, ADAMTS2, MYOM3, ATP13A3,ARHGEF25, ADA, HTRA4, NES, CRH, ACY3, PLD4, SCT, NOX4, PACSIN1,SERPINF1, SKIL, SEMA3G, TIPARP, LRRC26, PHEX, LILRA4, and PER1.

The present invention includes a circulating RNA (C-RNA) signature foran elevated risk of preeclampsia, the C-RNA signature encoding at leasta portion of any one or more, any two or more, any three or more, anyfour or more, any five or more, any six or more, any seven or more, anyeight or more, any nine or more, any ten or more, any eleven or more,any twelve, any thirteen or more, any fourteen or more, any fifteen ormore, any sixteen or more, any seventeen or more, any eighteen or more,any nineteen or more, any twenty or more, any twenty one or more, anytwenty two or more, any twenty three or more, any twenty four or more,any twenty five or more, any twenty-six or more, any twenty-seven ormore, any twenty-eight or more, any twenty-nine or more, any thirty ormore, any thirty-one or more, any thirty-two or more, any thirty-threeor more, any thirty-four or more, any thirty-five or more, anythirty-six or more, any thirty-seven or more, any thirty-eight or more,any thirty-nine or more, any forty or more, any forty-one or more, anyforty-two or more, any forty-three or more, any forty-four or more, anyforty-five or more, any forty-six or more, any forty-seven or more, anyforty-eight or more, or all forth-nine of those listed in Table S9 ofExample 7.

The present invention includes a circulating RNA (C-RNA) signature foran elevated risk of preeclampsia, the C-RNA signature encoding at leasta portion of any one or more, any two or more, any three or more, anyfour or more, any five or more, any six or more, any seven or more, anyeight or more, any nine or more, any ten or more, any eleven or more,any twelve or more, or all thirteen of AKAP2, ARRB1, CPSF7, INO80C,JAG1, MSMP, NR4A2, PLEK, RAP1GAP2, SPEG, TRPS1, UBE2Q1, and ZNF768.

The present invention includes a solid support array comprising aplurality of agents capable of binding and/or identifying a C-RNAsignature as described herein.

The present invention includes a kit comprising a plurality of probescapable of binding and/or identifying a C-RNA signature as describedherein.

The present invention includes a kit comprising a plurality of primersfor selectively amplifying a C-RNA signature as described herein.

As used herein, the term “nucleic acid” is intended to be consistentwith its use in the art and includes naturally occurring nucleic acidsor functional analogs thereof. Particularly useful functional analogsare capable of hybridizing to a nucleic acid in a sequence specificfashion or capable of being used as a template for replication of aparticular nucleotide sequence. Naturally occurring nucleic acidsgenerally have a backbone containing phosphodiester bonds. An analogstructure can have an alternate backbone linkage including any of avariety of those known in the art. Naturally occurring nucleic acidsgenerally have a deoxyribose sugar (e.g. found in deoxyribonucleic acid(DNA)) or a ribose sugar (e.g. found in ribonucleic acid (RNA)). Anucleic acid can contain any of a variety of analogs of these sugarmoieties that are known in the art. A nucleic acid can include native ornon-native bases. In this regard, a native deoxyribonucleic acid canhave one or more bases selected from the group consisting of adenine,thymine, cytosine or guanine and a ribonucleic acid can have one or morebases selected from the group consisting of uracil, adenine, cytosine orguanine. Useful non-native bases that can be included in a nucleic acidare known in the art. The term “template” and “target,” when used inreference to a nucleic acid, is intended as a semantic identifier forthe nucleic acid in the context of a method or composition set forthherein and does not necessarily limit the structure or function of thenucleic acid beyond what is otherwise explicitly indicated.

As used herein, “amplify,” “amplifying” or “amplification reaction” andtheir derivatives, refer generally to any action or process whereby atleast a portion of a nucleic acid molecule is replicated or copied intoat least one additional nucleic acid molecule. The additional nucleicacid molecule optionally includes sequence that is substantiallyidentical or substantially complementary to at least some portion of thetarget nucleic acid molecule. The target nucleic acid molecule can besingle-stranded or double-stranded and the additional nucleic acidmolecule can independently be single-stranded or double-stranded.Amplification optionally includes linear or exponential replication of anucleic acid molecule. In some embodiments, such amplification can beperformed using isothermal conditions; in other embodiments, suchamplification can include thermocycling. In some embodiments, theamplification is a multiplex amplification that includes thesimultaneous amplification of a plurality of target sequences in asingle amplification reaction. In some embodiments, “amplification”includes amplification of at least some portion of DNA and RNA basednucleic acids alone, or in combination. The amplification reaction caninclude any of the amplification processes known to one of ordinaryskill in the art. In some embodiments, the amplification reactionincludes polymerase chain reaction (PCR).

As used herein, “amplification conditions” and its derivatives,generally refers to conditions suitable for amplifying one or morenucleic acid sequences. Such amplification can be linear or exponential.In some embodiments, the amplification conditions can include isothermalconditions or alternatively can include thermocyling conditions, or acombination of isothermal and thermocycling conditions. In someembodiments, the conditions suitable for amplifying one or more nucleicacid sequences include polymerase chain reaction (PCR) conditions.Typically, the amplification conditions refer to a reaction mixture thatis sufficient to amplify nucleic acids such as one or more targetsequences, or to amplify an amplified target sequence ligated to one ormore adapters, e.g., an adapter-ligated amplified target sequence.Generally, the amplification conditions include a catalyst foramplification or for nucleic acid synthesis, for example a polymerase; aprimer that possesses some degree of complementarity to the nucleic acidto be amplified; and nucleotides, such as deoxyribonucleotidetriphosphates (dNTPs) to promote extension of the primer once hybridizedto the nucleic acid. The amplification conditions can requirehybridization or annealing of a primer to a nucleic acid, extension ofthe primer and a denaturing step in which the extended primer isseparated from the nucleic acid sequence undergoing amplification.Typically, but not necessarily, amplification conditions can includethermocycling; in some embodiments, amplification conditions include aplurality of cycles where the steps of annealing, extending andseparating are repeated. Typically, the amplification conditions includecations such as Mg′ or Mn′ and can also include various modifiers ofionic strength.

As used herein, the term “polymerase chain reaction” (PCR) refers to themethod of K. B. Mullis U.S. Pat. Nos. 4,683,195 and 4,683,202, whichdescribes a method for increasing the concentration of a segment of apolynucleotide of interest in a mixture of genomic DNA without cloningor purification. This process for amplifying the polynucleotide ofinterest consists of introducing a large excess of two oligonucleotideprimers to the DNA mixture containing the desired polynucleotide ofinterest, followed by a series of thermal cycling in the presence of aDNA polymerase. The two primers are complementary to their respectivestrands of the double-stranded polynucleotide of interest. The mixtureis denatured at a higher temperature first and the primers are thenannealed to complementary sequences within the polynucleotide ofinterest molecule. Following annealing, the primers are extended with apolymerase to form a new pair of complementary strands. The steps ofdenaturation, primer annealing and polymerase extension can be repeatedmany times (referred to as thermocycling) to obtain a high concentrationof an amplified segment of the desired polynucleotide of interest. Thelength of the amplified segment of the desired polynucleotide ofinterest (amplicon) is determined by the relative positions of theprimers with respect to each other, and therefore, this length is acontrollable parameter. By virtue of repeating the process, the methodis referred to as the “polymerase chain reaction” (hereinafter “PCR”).Because the desired amplified segments of the polynucleotide of interestbecome the predominant nucleic acid sequences (in terms ofconcentration) in the mixture, they are said to be “PCR amplified.” In amodification to the method discussed above, the target nucleic acidmolecules can be PCR amplified using a plurality of different primerpairs, in some cases, one or more primer pairs per target nucleic acidmolecule of interest, thereby forming a multiplex PCR reaction.

As used herein, the term “primer” and its derivatives refer generally toany polynucleotide that can hybridize to a target sequence of interest.Typically, the primer functions as a substrate onto which nucleotidescan be polymerized by a polymerase; in some embodiments, however, theprimer can become incorporated into the synthesized nucleic acid strandand provide a site to which another primer can hybridize to primesynthesis of a new strand that is complementary to the synthesizednucleic acid molecule. The primer can include any combination ofnucleotides or analogs thereof. In some embodiments, the primer is asingle-stranded oligonucleotide or polynucleotide. The terms“polynucleotide” and “oligonucleotide” are used interchangeably hereinto refer to a polymeric form of nucleotides of any length, and maycomprise ribonucleotides, deoxyribonucleotides, analogs thereof, ormixtures thereof. The terms should be understood to include, asequivalents, analogs of either DNA or RNA made from nucleotide analogsand to be applicable to single stranded (such as sense or antisense) anddouble-stranded polynucleotides. The term as used herein alsoencompasses cDNA, that is complementary or copy DNA produced from an RNAtemplate, for example by the action of reverse transcriptase. This termrefers only to the primary structure of the molecule. Thus, the termincludes triple-, double- and single-stranded deoxyribonucleic acid(“DNA”), as well as triple-, double- and single-stranded ribonucleicacid (“RNA”).

As used herein, the terms “library” and “sequencing library” refer to acollection or plurality of template molecules which share commonsequences at their 5′ ends and common sequences at their 3′ ends. Thecollection of template molecules containing known common sequences attheir 3′ and 5′ ends may also be referred to as a 3′ and 5′ modifiedlibrary.

The term “flowcell” as used herein refers to a chamber comprising asolid surface across which one or more fluid reagents can be flowed.Examples of flowcells and related fluidic systems and detectionplatforms that can be readily used in the methods of the presentdisclosure are described, for example, in Bentley et al., Nature456:53-59 (2008), WO 04/018497; U.S. Pat. No. 7,057,026; WO 91/06678; WO07/123744; U.S. Pat. Nos. 7,329,492; 7,211,414; 7,315,019; 7,405,281,and US 2008/0108082.

As used herein, the term “amplicon,” when used in reference to a nucleicacid, means the product of copying the nucleic acid, wherein the producthas a nucleotide sequence that is the same as or complementary to atleast a portion of the nucleotide sequence of the nucleic acid. Anamplicon can be produced by any of a variety of amplification methodsthat use the nucleic acid, or an amplicon thereof, as a templateincluding, for example, PCR, rolling circle amplification (RCA),ligation extension, or ligation chain reaction. An amplicon can be anucleic acid molecule having a single copy of a particular nucleotidesequence (e.g. a PCR product) or multiple copies of the nucleotidesequence (e.g. a concatameric product of RCA). A first amplicon of atarget nucleic acid is typically a complimentary copy. Subsequentamplicons are copies that are created, after generation of the firstamplicon, from the target nucleic acid or from the first amplicon. Asubsequent amplicon can have a sequence that is substantiallycomplementary to the target nucleic acid or substantially identical tothe target nucleic acid.

As used herein, the term “array” refers to a population of sites thatcan be differentiated from each other according to relative location.Different molecules that are at different sites of an array can bedifferentiated from each other according to the locations of the sitesin the array. An individual site of an array can include one or moremolecules of a particular type. For example, a site can include a singletarget nucleic acid molecule having a particular sequence or a site caninclude several nucleic acid molecules having the same sequence (and/orcomplementary sequence, thereof). The sites of an array can be differentfeatures located on the same substrate. Exemplary features includewithout limitation, wells in a substrate, beads (or other particles) inor on a substrate, projections from a substrate, ridges on a substrateor channels in a substrate. The sites of an array can be separatesubstrates each bearing a different molecule. Different moleculesattached to separate substrates can be identified according to thelocations of the substrates on a surface to which the substrates areassociated or according to the locations of the substrates in a liquidor gel. Exemplary arrays in which separate substrates are located on asurface include, without limitation, those having beads in wells.

The term “Next Generation Sequencing (NGS)” herein refers to sequencingmethods that allow for massively parallel sequencing of clonallyamplified molecules and of single nucleic acid molecules. Non-limitingexamples of NGS include sequencing-by-synthesis using reversible dyeterminators, and sequencing-by-ligation.

The term “sensitivity” as used herein is equal to the number of truepositives divided by the sum of true positives and false negatives.

The term “specificity” as used herein is equal to the number of truenegatives divided by the sum of true negatives and false positives.

The term “enrich” herein refers to the process of amplifying nucleicacids contained in a portion of a sample. Enrichment includes specificenrichment that targets specific sequences, e.g., polymorphic sequences,and non-specific enrichment that amplifies the whole genome of the DNAfragments of the sample.

As used herein, the term “each,” when used in reference to a collectionof items, is intended to identify an individual item in the collectionbut does not necessarily refer to every item in the collection unlessthe context clearly dictates otherwise.

As used herein, “providing” in the context of a composition, an article,a nucleic acid, or a nucleus means making the composition, article,nucleic acid, or nucleus, purchasing the composition, article, nucleicacid, or nucleus, or otherwise obtaining the compound, composition,article, or nucleus.

The term “and/or” means one or all of the listed elements or acombination of any two or more of the listed elements.

The words “preferred” and “preferably” refer to embodiments of thedisclosure that may afford certain benefits, under certaincircumstances. However, other embodiments may also be preferred, underthe same or other circumstances. Furthermore, the recitation of one ormore preferred embodiments does not imply that other embodiments are notuseful, and is not intended to exclude other embodiments from the scopeof the disclosure.

The terms “comprises” and variations thereof do not have a limitingmeaning where these terms appear in the description and claims.

It is understood that wherever embodiments are described herein with thelanguage “include,” “includes,” or “including,” and the like, otherwiseanalogous embodiments described in terms of “consisting of” and/or“consisting essentially of” are also provided.

Unless otherwise specified, “a,” “an,” “the,” and “at least one” areused interchangeably and mean one or more than one.

Also herein, the recitations of numerical ranges by endpoints includeall numbers subsumed within that range (e.g., 1 to 5 includes 1, 1.5, 2,2.75, 3, 3.80, 4, 5, etc.).

Reference throughout this specification to “one embodiment,” “anembodiment,” “certain embodiments,” or “some embodiments,” etc., meansthat a particular feature, configuration, composition, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment of the disclosure. Thus, the appearances of such phrases invarious places throughout this specification are not necessarilyreferring to the same embodiment of the disclosure. Furthermore, theparticular features, configurations, compositions, or characteristicsmay be combined in any suitable manner in one or more embodiments.

For any method disclosed herein that includes discrete steps, the stepsmay be conducted in any feasible order. And, as appropriate, anycombination of two or more steps may be conducted simultaneously.

The above summary of the present disclosure is not intended to describeeach disclosed embodiment or every implementation of the presentdisclosure. The description that follows more particularly exemplifiesillustrative embodiments. In several places throughout the application,guidance is provided through lists of examples, which examples can beused in various combinations. In each instance, the recited list servesonly as a representative group and should not be interpreted as anexclusive list.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 . A schematic of the relationships between placental health,maternal response, and fetal response.

FIG. 2 . Origins of circulating RNA (C-RNA).

FIG. 3 . Library prep workflow for C-RNA.

FIG. 4 . Validation of C-RNA approach comparing 3rd trimester pregnantand non-pregnant samples.

FIG. 5 . Validation of C-RNA approach using longitudinal pregnancysamples.

FIG. 6 . Description of clinical studies.

FIG. 7 . Sequencing data characteristics.

FIG. 8 . Classification of PE without any selection of genes, relying ofentire data set.

FIG. 9 . Description of bootstrapping method.

FIG. 10 . Classification of preeclampsia samples with bootstrappingapproach.

FIG. 11 . Examination of over-abundant preeclampsia genes.

FIG. 12 . Standard Adaboost Model.

FIG. 13 . Independent cohort allows further validation of preeclampsiasignature.

FIG. 14 . Performance of standard adaboost model in classification ofpreeclampsia.

FIG. 15 . Classification of preeclampsia with standard DEX TREATanalysis.

FIG. 16 . Selection of genes and classification of preeclampsia withjackknifing approach.

FIG. 17 . Validation of TREAT, Bootstrapping, and Jackknifing approachesin independent PEARL biobank cohort.

FIG. 18 . A diagram of the bioinformatic approach to build AdaBoostRefined models.

FIG. 19 . Relative abundance of genes utilized by AdaBoost Refined modeland their predictive capability on independent datasets.

FIG. 20 . Identifying C-RNA signatures specific to preeclampsia inNextera Flex generated libraries using standard TREAT analysis andjackknifing approach.

FIG. 21 . Relative abundance of genes utilized by AdaBoost Refined modelon Nextera Flex generated libraries and their predictive power in RGH14dataset.

FIGS. 22A-22D. Validation of a clinic-friendly, whole-exome C-RNAanalysis method. FIG. 22A is a schematic of the sequencing librarypreparation method; all steps after blood collection can be performed ina centralized processing lab. Temporal changes of transcripts alteredthroughout the course of pregnancy (FIG. 22B). Overlap of genesidentified in C-RNA pregnancy progression studies (FIG. 22C). Tissuesexpressing the 91 genes unique to the pregnancy time course study (FIG.22D).

FIGS. 23A-23C. Sample collection for PE clinical studies. Panelsillustrate the time of blood collection (triangles) and gestational ageat birth (squares) for each individual in the iPC study (FIG. 23A) andthe PEARL study (FIG. 23B). The red line indicates the threshold forterm birth. Preterm birth rates are significantly elevated inearly-onset PE cohorts (FIG. 23C). *** p<0.001 by Fisher's exact test.

FIGS. 24A-24G. Differential analysis of C-RNA identifies preeclampsiabiomarkers. Fold change and abundance of transcripts altered in PE (FIG.24A). One-sided confidence p-value intervals were calculated afterjackknifing for each gene detected by standard analysis methods (FIG.24B). Transcript abundance fold-change determined by whole exomesequencing and by qPCR for (21) genes (FIG. 24C). * p<0.05 by Student'sT-test. Tissue distribution of affected genes (FIG. 24D). Hierarchicalclustering of iPC samples (average linkage, squared Euclidean distance)(FIG. 24E). Clustering of early-onset PE (FIG. 24F) and late-onset PE(FIG. 24G) samples from the PEARL study.

FIGS. 25A-25E. AdaBoost classifies preeclampsia samples across cohorts.Heatmap illustrating the relative abundance of the transcripts used bymachine learning in each cohort (FIG. 25A). The height of each blockreflects each gene's importance. ROC curves for each dataset (FIG. 25B).Distributions (KDE) of AdaBoost Scores. The orange line indicates theoptimal boundary to discriminate PE and control samples (FIG. 25C).Concordance of genes identified by differential analysis and those usedin AdaBoost (FIG. 25D). Tissue distribution of AdaBoost genes (FIG.25E).

FIGS. 26A-26C. C-RNA data integrity when blood is stored in differentcollection tubes. Comparing the abundance of previously detected C-RNApregnancy markers from blood stored overnight in different tube types toimmediate processing after collection in EDTA tubes (FIG. 26A).Scatterplots comparing transcript FPKM values for C-RNA prepared fromthe same individual after different blood storage durations (FIG. 26B).Pearson's correlation coefficient, R, is more variable when using EDTAtubes (cf, Cell-Free) (FIG. 26C).

FIGS. 27A and 27B. The effect of plasma volume on C-RNA data quality. Ameta-analysis was performed with data from nine independent studies todetermine the appropriate plasma input for the protocol. Noise(biological coefficient of variation, EdgeR) was calculated frombiological replicates within each study (FIG. 27A). Library complexity(bound population, Preseq) was calculated for each sample (FIG. 27B). **p<0.01, *** p<0.001 by ANOVA with Tukey's HSD correction, with study asa blocking variable.

FIGS. 28A-28C. Pregnancy marker tissue specificity. Pie charts showingtissue specificity of the genes detected in pregnancy by threeindependent studies, using either the full set of altered genes (FIG.28A), the transcripts unique to each study (FIG. 28B), or intersectinggene sets (FIG. 28C).

FIGS. 29A-29E. Jackknifing excludes genes that are not universallyaltered in preeclampsia. Schematic of the jackknifing approach used todetermine how consistently transcripts were altered across PE samples(FIG. 29A). Average abundance and noise for each differentially abundantgene (FIG. 29B). ROC area under the curve values for each affectedtranscript provide a measure of how separated C-RNA transcript abundancedistributions are for control and PE samples (FIG. 29C). * p<0.05 byMann-Whitney U test. Hierarchical clustering of iPC samples using thegenes excluded after jackknifing (FIG. 29D). Tissue distribution ofexcluded transcripts (FIG. 29E). The decreased contribution of the fetusand placenta may suggest the maternal component of PE is most variablebetween individuals.

FIGS. 30A-30D. AdaBoost model development strategy. The RGH014 datasetwas divided into 6 pieces (FIG. 30A). The “Holdout Subset” contained 10%of the samples (randomly selected) as well as the 3 samples which wereincorrectly clustered when using differentially abundant genes (as withFIG. 24C) and was fully excluded from model building. The remainingsamples were divided at random into 5 evenly sized “Test Subsets.” Foreach test subset, training data was composed of all non-holdout andnon-testing samples. Gene counts for training and testing data wereTMM-normalized in edgeR, and then standardized to mean 0 and standarddeviation 1 for each gene. For each train/test sample set, AdaBoostmodels (90 estimators, 1.6 learning rate) were built 10 times from thetraining data (FIG. 30B). Feature pruning was performed, removing genesbelow an incrementally increasing importance threshold and assessingperformance by Matthew's correlation coefficient when predicting testingdata. The model with the best performance—and fewest genes, in the caseof a tie—was retained. Estimators from all 50 independent models werecombined into a single AdaBoost model (FIG. 30C). Feature pruning wasperformed on the resulting ensemble, this time using the percent ofmodels which used a gene to set threshold values and performancemeasured by average log loss value across test subsets. ROC curve afterapplying the final AdaBoost model to the holdout data (FIG. 30D). Allsamples segregated correctly, except for two of the three samples whichalso misclustered by HCA.

FIGS. 31A-31E. The effect of hyperparameter selection and featurepruning on machine learning performance. Heatmap of a grid search toidentify the optimal hyperparameters for AdaBoost (FIG. 31A). Matthew'scorrelation coefficient was used as a measure of performance. Flattenedviews of performance for each hyperparameter (FIG. 31B). Arrows indicatethe values selected for model construction. FIG. 31C shows the impact ofpruning individual AdaBoost models on performance (as in FIG. 30B).Solid lines are the average for all 10 models, and the shaded regionshows the standard deviation. The number of AdaBoost models using eachgene observed in the pre-pruned ensemble (FIG. 31D). Model performancewhen pruning the combined AdaBoost ensemble (FIG. 31E). The orange linesin FIG. 31D and FIG. 31E show the threshold applied to generate thefinal AdaBoost model.

FIGS. 32A-32C. Changes in the C-RNA transcriptome track with pregnancyprogression. FIG. 32A shows temporal changes in transcriptssignificantly altered throughout the course of pregnancy. Each rowcorresponds to a transcript, the abundance of which was normalizedacross all samples (N=152) prior to clustering. Orange signifieselevated abundance; purple indicates decreased abundance. FIG. 32B showsthe overlap of transcripts identified in three independent C-RNApregnancy progression analyses. N=number of plasma samples collectedfrom a pregnant woman in cohort. FIG. 32C shows tissues expressing the91 genes that were only detected with the PEARL HCC cohort.

FIGS. 33A-33C. Sample collection but not clinical outcome is matched forcontrol and PE samples. Panels illustrate the time of blood collection(triangles) and gestational age at birth (squares) for each individualin the iPEC study (FIG. 33A) and the PEARL PEC study (FIG. 33B). The redline indicates the threshold for term birth at 37 weeks. As shown inFIG. 33C, preterm birth rates are significantly elevated in early-onsetPE cohorts. *** p<0.001 by Fisher's exact test. iPEC, Control N=73, PEN=40; PEARL PEC, N=12 for each group.

FIGS. 34A-34F. Applying jackknifing to differential expression analysisexcludes genes with lower sensitivity for identifying PE samples. FIG.34A shows fold change and abundance of transcripts altered in PE. FIG.34B shows one-sided, normal-based 95% confidence intervals of thep-value for each transcript that was detected as altered by standardanalyses. FIG. 34C is a schematic of the jackknifing approach, wherein90% of samples were randomly selected for analysis over many iterationsto quantify p-value stability. FIG. 34D shows average abundance andnoise for each differentially abundant gene (p>0.05 for both variablesby Mann-Whitney U; N=30,12). FIG. 34E shows ROC area under the curvevalues for each affected transcript reflect how separated C-RNAtranscript abundance distributions are for control versus PE samples (*p<0.05 by Mann-Whitney U; Included, N=30; Excluded, N=12). FIG. 34Fshows hierarchical clustering of iPEC samples using the genes excludedafter jackknifing (sensitivity=73%; specificity=99%; N=113). For FIGS.34A, 34B, 34D, and 34E orange and blue datapoints reflect transcriptsconsidered statistically altered in PE by standard differentialexpression analysis, but only orange datapoint were identified by thejackknifing approach. Sample status is shown by the blue (PE) and gray(control) rectangles along the right side of the heatmap.

FIGS. 35A-35E. Ubiquitously altered C-RNA transcripts segregateearly-onset PE samples from controls. FIG. 35A shows the fold-changebetween PE and control pregnancies was assessed both by sequencing(orange) and by qPCR (purple) for 20 transcripts (* p<0.05 by Student'sT-test; N=19 for control and for PE). FIG. 35B shows tissue expressionof affected genes. FIG. 35C shows hierarchical clustering of the iPECsamples (average linkage, squared Euclidean distance; PE, N=40; control,N=73). Clustering of early-onset (FIG. 35D) and late-onset (FIG. 35E) PEand control pregnancy samples from the PEARL PEC (N=12 for each group).

FIGS. 36A-36E. Machine learning accurately classifies PE acrossindependent cohorts. FIG. 36A shows average ROC curve for iPECvalidation samples (dashed line=SD; N=10). FIG. 36B shows accuracy,sensitivity and specificity measurements if iPEC hold out samples andindependent PEARL PEC samples (N=10). FIG. 36C is a heatmap of therelative transcript abundance in the iPEC cohort for the genes used byAdaBoost model. The graph on the right indicates how manycross-validation models a given transcript appeared in. FIG. 36D showsconcordance of transcripts identified by differential analysis andAdaBoost. FIG. 36E shows tissue expressing elevated levels oftranscripts selected by AdaBoost models.

FIGS. 37A and 37B. The C-RNA sample preparation workflow. FIG. 37A showsthe approach used for sequencing library preparation. Blood is shippedovernight prior to plasma processing and nucleic acid extraction. cfDNAis digested with Dnase, then cDNA is synthesized from all RNA. Wholetranscriptome enrichment is performed prior to sequencing. In FIG. 37B,three methods were assessed for C-RNA transcriptome analyses. rRNAdepletion did not consistently enrich the exonic C-RNA fraction; manylibraries contain numerous unaligned reads. Likewise, rRNA overwhelmedsequencing datasets when not removed. Enrichment generated librarieswith the highest proportion of reads from exonic C-RNA. For all bargraphs, orange shows reads aligned to the human genome, gray shows readsaligned to an rRNA sequence, and pink shows reads that do not align tothe human genome (including both non-human RNA and low qualitysequences).

FIGS. 38A-38C. The effect of plasma volume on C-RNA data quality. FIG.38A shows the C-RNA yield in plasma from 122 samples was quantified withthe Quant-iT RiboGreen assay (Thermo Fisher). Measurements from 23samples were below the detection threshold and are excluded from thegraph. Bars show mean±SD from 2 technical replicates. Data from 9independent experiments was used for a meta-analysis evaluating theeffect of plasma input on data quality. FIG. 38B shows noise (biologicalcoefficient of variation, edgeR) calculated from biological replicateswithin each study. FIG. 38C shows library complexity (bound population,preseq) of each sample. ** p<0.01, *** p<0.001 by ANOVA with Tukey's HSDcorrection, using the study as a blocking variable. 0.5 mL, N=8, 95; 1mL, N=7, 83; 2 mL, N=7, 33; 4 mL, N=17, 267 for FIGS. 38B and 38C,respectively.

FIGS. 39A-39E. The integrity of C-RNA pregnancy signal after storage indifferent BCTs. FIG. 39A is a heatmap of the abundance of known C-RNApregnancy markers after overnight storage in 4 BCTs compared toimmediate processing from EDTA BCTs. FIG. 39B shows an integratedmeasure of pregnancy signal obtained by summing transcript abundance inFIG. 39A discriminates between pregnant and non-pregnant samples. **p<0.01, *** p<0.001 by ANOVA with Tukey's HSD correction. EDTAimmediate, N=4, 8; EDTA overnight, N=7, 7; ACD overnight, N=16, 16;Cell-Free RNA overnight, N=10, 9; Cell-Free DNA overnight, N=8, 8; fornon-pregnant and pregnant groups, respectively. FIG. 39C showscorrelation of transcriptomic profiles from blood samples collected fromthe same individual and stored for 0, 1 or 5 days in Cell-free DNA BCTs(Streck, Inc) prior to processing. Bars show mean±range; N=2. TheAdaBoost scores assigned to control samples (FIG. 39D) or to PE samples(FIG. 39E) from the iPEC cohort versus the number of days blood wasstored at room temperature prior to plasma processing. AdaBoost scoresare normalized to range from −1 to +1, with control samples expected tohave a score <0, and PE samples to have a score >0. No significantdifferences in AdaBoost score were observed (ANOVA for controls; T-Testfor PE). Control 1 day, N=60; 2 days, N=4; 3 days, N=1, 5 days, N=2. PE1 day, N=37; 2 days, N=3.

FIGS. 40A-40D. C-RNA transcripts can be altered during specific stagesof pregnancy. Dynamic changes in transcripts that primarily change earlyin pregnancy (FIG. 40A, ˜14 weeks), throughout gestation (FIG. 40B), orpredominantly late in pregnancy (FIG. 40C, ˜33 weeks). Note howtranscripts which change from the first to second trimester do notreturn to baseline levels but remain at the altered abundance for theremainder of gestation. FIG. 40D is an ontology and pathway enrichmentanalysis for transcripts which change during healthy pregnancy. Eachfilled in box signifies significant enrichment of the corresponding termor pathway. “All Genes” shows analysis of all 156 differentiallyabundant transcripts. Too few genes were altered late in pregnancy toperform ontological analysis.

FIGS. 41A-41C. Comparing pregnancy-associated transcript tissuespecificity for three independent C-RNA studies. FIG. 41A shows tissuespecificity for the full set of genes detected in each study. FIG. 41Bshows the transcripts unique to each study. FIG. 41C shows intersectinggene sets.

FIGS. 42A and 42B. AdaBoost hyperparameter optimization. Performance,measured by Matthew's Correlation Coefficient, versus the number ofestimators (FIG. 42A) or the learning rate (FIG. 42B). Each dot showsthe average value obtained from 3-fold cross validation during therandom search.

FIGS. 43A-43D. AdaBoost training strategy. In FIG. 43A, the trainingsamples are divided into 5 evenly sized subsets, corresponding to fiveiterations of model construction. For the first iteration, the samplesin subset 1 are used for pruning while the samples in subsets 2, 3, 4,and 5 are combined for AdaBoost fitting; for the second iteration thesamples in subset 2 are used for pruning while those in subsets 1, 3, 4,and 5 are used for AdaBoost fitting; and so on for the remaining 3iterations. As shown in FIG. 43B, for each iteration, an AdaBoost modelis fit to the “Fitting Samples.” Then the impact of removing genes belowan incrementally increasing importance threshold on classificationperformance is assessed with the “Pruning Samples.” The model with thebest performance and fewest genes is retained. In FIG. 43C, the processin FIG. 43B is repeated 10 times for each set of fitting and pruningsamples, generating a total of 50 models. In FIG. 43D, estimators fromall models are then aggregated into a single AdaBoost ensemble. Featurepruning of the aggregate model is performed to identify the minimal geneset required for optimal classification.

FIGS. 44A-44D. AdaBoost output is significantly impacted by sampleselection. FIG. 44A shows classification performance (log-loss) of theindividual models generated from each AdaBoost subset (* p<0.05, ***p<0.001 by ANOVA with Tukey's HSD correction; N=10 each). In FIG. 44B,the frequency each transcript was included in one of the 50 separateAdaBoost models. FIG. 44C is a Venn diagram of the transcriptsincorporated in each training subset's models. While 5 transcripts areutilized in models from all subsets, 40 are unique to a single subset.FIG. 44D shows the effect of pruning estimators on classificationperformance (log-loss) for the final, fully aggregated AdaBoost modelalso shows distinct behavior for each set of samples. These trends areparticularly striking when considering that 75% of the data used forfitting AdaBoost models were shared by any two sets of samples used forfitting AdaBoost. Note these data were generated separately from thefinal machine learning analysis presented in FIG. 41 .

The schematic drawings are not necessarily to scale. Like numbers usedin the figures may refer to like components. However, it will beunderstood that the use of a number to refer to a component in a givenfigure is not intended to limit the component in another figure labeledwith the same number. In addition, the use of different numbers to referto components is not intended to indicate that the different numberedcomponents cannot be the same or similar to other numbered components.

DETAILED DESCRIPTION

Provided herein are signatures of circulating RNA found in the maternalcirculation that are specific to preeclampsia and the use of suchsignatures in noninvasive methods for the diagnosis of preeclampsia andthe identification of pregnant women at risk for developingpreeclampsia.

While most of the DNA and RNA in the body is located within cells,extracellular nucleic acids can also be found circulating freely in theblood. Circulating RNA, also referred to herein as “C-RNA,” refers toextracellular segments of RNA found in the bloodstream. C-RNA moleculesoriginate predominately from two sources: one, released into thecirculation from dying cells undergoing apoptosis, and two, containedwithin exosomes shed by living cells into the circulation. Exosomes aresmall membranous vesicles about 30-150 nm of diameter released from manycell types into the extracellular space and are found in a wide varietyof body fluids, including serum, urine, and breast milk and carryingprotein, mRNA, and microRNA. The lipid bilayer structure of exosomesprotects the RNAs contained within from degradation by RNases, providingfor stability in blood. See, for example, Huang et al., 2013, BMCGenomics; 14:319; And Li et al., 2017, Mol Cancer; 16:145). Evidence isaccumulating that exosomes have specialized functions and play a role insuch processes as coagulation, intercellular signaling, and wastemanagement (van der Pol et al., 2012, Pharmacol Rev; 64(3):676-705).See, also, Samos et al., 2006, Ann N Y Acad Sci; 1075:165-173; Zerneckeet al., 2009, Sci Signal; 2:ra81; Ma et al., 2012, J Exp Clin CancerRes; 31:38; and Sato-Kuwabara et al., 2015, Int J Oncol; 46:17-27.

With the methods described herein, the C-RNA molecules found in maternalcirculation function as biomarkers of fetal, placental, and maternalhealth and provide a window into the progression of pregnancy. Describedherein are C-RNA signatures within the maternal circulation that areindicative of pregnancy, C-RNA signatures within the maternalcirculation that are temporally associated with the gestational stage ofpregnancy, and C-RNA signatures within the maternal circulation that areindicative of the pregnancy complication preeclampsia.

A C-RNA signature within the maternal circulation indicative ofpreeclampsia includes a plurality of C-RNA molecules encoding at least aportion of a plurality of proteins selected from ARRDC2, JUN, SKIL,ATP13A3, PDE8B, GSTA3, PAPPA2, TIPARP, LEP, RGP1, USP54, CLEC4C, MRPS35,ARHGEF25, CUX2, HEATR9, FSTL3, DDI2, ZMYM6, ST6GALNAC3, GBP2, NES, ETV3,ADAM17, ATOH8, SLC4A3, TRAF3IP1, TTC21A, HEG1, ASTE1, TMEM108, ENC1,SCAMP1, ARRDC3, SLC26A2, SLIT3, CLIC5, TNFRSF21, PPP1R17, TPST1, GATSL2,SPDYE5, HIPK2, MTRNR2L6, CLCN1, GINS4, CRH, C10orf2, TRUB1, PRG2, ACY3,FAR2, CD63, CKAP4, TPCN1, RNF6, THTPA, FOS, PARN, ORAI3, ELMO3, SMPD3,SERPINF1, TMEM11, PSMD11, EBI3, CLEC4M, CCDC151, CPAMD8, CNFN, LILRA4,ADA, C22orf39, PI4KAP1, and ARFGAP3. This C-RNA signature is theAdaboost General signature obtained with the TruSeq library prep methodshown in Table 1 below, also referred to herein as “list (a)” or “(a).”

A C-RNA signature within the maternal circulation indicative ofpreeclampsia includes a plurality of C-RNA molecules encoding at least aportion of a plurality of proteins selected from TIMP4, FLG, HTRA4,AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2, SEMA3G, ADAMTS1, ALOX15B,SLC9A3R2, TIMP3, IGFBP5, HSPA12B, CLEC4C, KRT5, PRG2, PRX, ARHGEF25,ADAMTS2, DAAM2, FAM107A, LEP, NES, and VSIG4. This C-RNA signature isthe Bootstrapping signature obtained with the TruSeq library prep methodshown in Table 1 below, also referred to herein as “list (b)” or “(b).”

A C-RNA signature within the maternal circulation indicative ofpreeclampsia includes a plurality of C-RNA molecules encoding at least aportion of a protein selected from CYP26B1, IRF6, MYH14, PODXL, PPP1R3C,SH3RF2, TMC7, ZNF366, ADCY1, C6, FAM219A, HAO2, IGIP, IL1R2, NTRK2,SH3PXD2A, SSUH2, SULT2A1, FMO3, FSTL3, GATA5, HTRA1, C8B, H19, MN1,NFE2L1, PRDM16, AP3B2, EMP1, FLNC, STAG3, CPB2, TENC1, RP1L1, A1CF,NPR1, TEK, ERRFI1, ARHGEF15, CD34, RSPO3, ALPK3, SAMD4A, ZCCHC24, LEAP2,MYL2, NRG3, ZBTB16, SERPINA3, AQP7, SRPX, UACA, ANO1, FKBP5, SCN5A,PTPN21, CACNA1C, ERG, SOX17, WWTR1, AIF1L, CA3, HRG, TAT, AQP7P1,ADRA2C, SYNPO, FN1, GPR116, KRT17, AZGP1, BCL6B, KIF1C, CLIC5, GPR4,GJA5, OLAH, C14orf37, ZEB1, JAG2, KIF26A, APOLD1, PNMT, MYOM3, PITPNM3,TIMP4, HTRA4, AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2, SEMA3G, ADAMTS1,ALOX15B, SLC9A3R2, TIMP3, IGFBP5, HSPA12B, PRG2, PRX, ARHGEF25, ADAMTS2,DAAM2, FAM107A, LEP, NES, VSIG4, HBG2, CADM2, LAMPS, PTGDR2, NOMO1,NXF3, PLD4, BPIFB3, PACSIN1, CUX2, FLG, CLEC4C, and KRT5. This C-RNAsignature is the Standard DEX Treat signature obtained with the TruSeqlibrary prep method shown in Table 1 below, also referred to herein as“list (c)” or “(c).”

A C-RNA signature within the maternal circulation indicative ofpreeclampsia includes a plurality of C-RNA molecules encoding at least aportion of a protein selected from VSIG4, ADAMTS2, NES, FAM107A, LEP,DAAM2, ARHGEF25, TIMP3, PRX, ALOX15B, HSPA12B, IGFBP5, CLEC4C, SLC9A3R2,ADAMTS1, SEMA3G, KRT5, AMPH, PRG2, PAPPA2, TEAD4, CRH, PITPNM3, TIMP4,PNMT, ZEB1, APOLD1, PLD4, CUX2, and HTRA4. This C-RNA signature is theJacknifing signature obtained with the TruSeq library prep method shownin Table 1 below, also referred to herein as “list (d)” or “(d).”

A C-RNA signature within the maternal circulation indicative ofpreeclampsia includes a plurality of C-RNA molecules encoding at least aportion of a protein selected from ADAMTS1, ADAMTS2, ALOX15B, AMPH,ARHGEF25, CELF4, DAAM2, FAM107A, HSPA12B, HTRA4, IGFBP5, KCNA5, KRT5,LCN6, LEP, LRRC26, NES, OLAH, PACSIN1, PAPPA2, PRX, PTGDR2, SEMA3G,SLC9A3R2, TIMP3, and VSIG4. This C-RNA signature is the Standard DEXTreat signature obtained with the Nextera Flex for Enrichment libraryprep method shown in Table 1 below, also referred to herein as “list(e)” or “(e).”

A C-RNA signature within the maternal circulation indicative ofpreeclampsia includes a plurality of C-RNA molecules encoding at least aportion of a protein selected from ADAMTS1, ADAMTS2, ALOX15B, ARHGEF25,CELF4, DAAM2, FAM107A, HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26,NES, OLAH, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, and VSIG4. This C-RNAsignature is the Jacknifing signature obtained with the Nextera Flex forEnrichment library prep method shown in Table 1 below, also referred toherein as “list (f)” or “(f).”

A C-RNA signature within the maternal circulation indicative ofpreeclampsia includes a plurality of C-RNA molecules encoding at least aportion of a protein selected from CLEC4C, ARHGEF25, ADAMTS2, LEP,ARRDC2, SKIL, PAPPA2, VSIG4, ARRDC4, CRH, and NES.

This C-RNA signature is the Adaboost Refined TruSeq signature obtainedwith the TruSeq library prep method shown in Table 1 below, alsoreferred to herein as “AdaBoost Refined 1,” “list (g),” or “(g).”

In some embodiments, a C-RNA signature within the maternal circulationindicative of preeclampsia includes C-RNA molecules encoding at least aportion of a protein selected from ADAMTS2, ARHGEF25, ARRDC2, CLEC4C,LEP, PAPPA2, and VSIG4 (also referred to herein as “AdaBoost Refined2”), ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, SKIL, and VSIG4(also referred to herein as “AdaBoost Refined 3”), ADAMTS2, ARHGEF25,ARRDC4, CLEC4C, LEP, NES, SKIL, and VSIG4 (also referred to herein as“AdaBoost Refined 4”), ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, CRH,LEP, PAPPA2, SKIL, and VSIG4 (also referred to herein as “AdaBoostRefined 5”), ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, and SKIL (alsoreferred to herein as “AdaBoost Refined 6”), or ADAMTS2, ARHGEF25,ARRDC2, ARRDC4, CLEC4C, LEP, PAPPA2, and SKIL (also referred to hereinas “AdaBoost Refined 7”).

A C-RNA signature within the maternal circulation indicative ofpreeclampsia includes a plurality of C-RNA molecules encoding at least aportion of a protein selected from LEP, PAPPA2, KCNA5, ADAMTS2, MYOM3,ATP13A3, ARHGEF25, ADA, HTRA4, NES, CRH, ACY3, PLD4, SCT, NOX4, PACSIN1,SERPINF1, SKIL, SEMA3G, TIPARP, LRRC26, PHEX, LILRA4, and PER1. ThisC-RNA signature is the Adaboost Refined Nextera Flex signature obtainedwith the Nextera Flex for Enrichment library prep method shown in Table1 below, also referred to herein as “list (h)” or “(h).”

A C-RNA signature within the maternal circulation indicative ofpreeclampsia includes a plurality of C-RNA molecules encoding at least aportion of a protein selected from any of those shown Table S9 ofExample 7, also referred to herein as “list (i)” or “(i).”

A C-RNA signature within the maternal circulation indicative ofpreeclampsia includes a plurality of C-RNA molecules encoding at least aportion of a protein selected from AKAP2, ARRB1, CPSF7, INO80C, JAG1,MSMP, NR4A2, PLEK, RAP1GAP2, SPEG, TRPS1, UBE2Q1, and ZNF768, alsoreferred to herein as “list (j)” or “(j).”

In some embodiments, a C-RNA signature within the maternal circulationindicative of preeclampsia includes a plurality of C-RNA moleculesencoding at least a portion of a protein selected from of any one ormore of any of (a), (b), (c), (d), (e), (f), (g), (h), (i), and/or (j)in combination with any one or more of any of (a), (b), (c), (d), (e),(f), (g), (h), (i), and/or (j).

The Examples provided herewith describe the eight gene lists summarizedabove that distinguish preeclampsia and control pregnancies. Each wasidentified by using different analysis methods and/or distinct datasets.However, there is a high degree of concordance between many of thesegene sets. Identifying a transcript as altered in preeclampsia C-RNAwith multiple approaches indicates that said transcript has higherpredictive value for classification of this disease. Thus, theimportance of the transcripts identified by all differential expressionanalyses and by all AdaBoost models was combined and ranked. Genesassigned lower ranks are not unimportant or uninformative, but they maybe less robust for classification of preeclampsia across cohorts andsample preparations.

First, the transcripts identified when using all differential expressionanalyses (Standard DEX Treat, bootstrapping and the jackknifing) forboth library preparation methods (TruSeq and Nextera Flex forEnrichment) were combined. Table 2 below shows the relative importancefor all of the 125 transcripts identified by the different analysismethods. Transcripts identified across every analysis method and bothlibrary preparations are the strongest classifiers and assigned animportance ranking of 1. Transcripts that were identified by three ormore analysis methods and were detected with both library preparationswere given an importance ranking of 2. Transcripts identified by themost stringent analysis method, jackknifing but only one of the librarypreparations were assigned an importance ranking of 3. Transcriptsidentified in two of the five analysis methods were given an importanceranking of 4. Transcripts that were only identified in the Standard DEXTreat method, the most broad and inclusive analysis, were given thelowest importance ranking of 5.

Then, the 91 transcripts identified across all AdaBoost models (AdaBoostGeneral and AdaBoost Refined) and both library preparations (Table 3below) were combined. When generating the refined AdaBoost models foreach library preparation, observed slight variations had been observedin the gene set obtained each time a model was built from the same data.This is a natural result of randomness used by AdaBoost to searchthrough the large whole-exome C-RNA data. To obtain a representativelist of genes, model building for refined AdaBoost was run a minimum ofnine separate times and all genes used by one or more models reported.The percent of models that included each transcript are reported inTable 3 (Frequency Used By AdaBoost). AdaBoost assigns its own“importance” value to each transcript, which reflects how much theabundance of that transcript influences determining whether a sample isfrom a preeclampsia patient. These AdaBoost importance values wereaveraged across each refined AdaBoost model a given transcript was usedby (Table 3, Average AdaBoost Model Importance).

Transcripts identified across all AdaBoost analyses and librarypreparations were assigned the highest importance ranking of 1.Transcripts identified in the refined AdaBoost model for a singlelibrary preparation method with over 90% frequency used by AdaBoost wereassigned an importance ranking of 2. Generally, these transcripts alsohave higher AdaBoost model importance, consistent with increasedpredictive capabilities. Transcripts identified in the refined AdaBoostmodel for a single library preparation method but used by less than 90%of AdaBoost models were assigned an importance ranking of 3. Transcriptsidentified only in the general AdaBoost model for a single librarypreparation were given the lowest importance ranking of 4.

Table 2 lists every gene identified by DEX analysis across all analysisapproaches and library preps. Rank 1=Transcript identified across everyanalysis method and library prep method. Rank 2=Transcript identifiedboth library preps, and 3/5 analysis methods. Rank 3=Identified in onelibrary prep method, in jacknifing, are most stringent analysis. Rank4=identified in 2/5 analyses. And Rank 5=Only identified in Standard DEXTreat method, our most relaxed analysis method.

Table 3 lists every gene identified by Adaboost analysis across bothlibrary preps. Rank 1=identified in both library prep methods and therefined adaboost models. Rank 2=Identified in one library prep method,present in refined adaboost model with high model importance andfrequency. Rank 3=identified in one library prep method, present inrefined adaboost model with medium model importance and frequency. AndRank 4=Identified in one library prep, not present in the refinedadaboost model.

Table 4 below is a glossary of all of the various genes recited herein.The information was obtained from the HUGO Gene Nomenclature Committeeat the European Bioinformatics Institute.

TABLE 1 Composite Gene Listing Machine Learning Approaches AdaboostAdaboost Refined DIFFERENTIAL EXPRESSION APPROACHES Refined Nextera FlexStandard DEX Jackknifing TruSeq Library Prep: Bootstrapping JackknifingTREAT Library Prep: Library Prep: Nextera Flex for Library Prep: LibraryPrep: Library Prep: Nextera Flex Adaboost TruSeq Enrichment Standard DEXTruSeq TruSeq Nextera Flex for Enrichment General Imrpoved modelImrpoved model TREAT Refined Refined for Enrichment Refined subset ofLibrary Prep: building for building for Library Prep: TruSeq subsetsubset Broadest Standard DEX TruSeq Adaboost, to Adaboost, to BroadestDEX list of Standard of Standard DEX list TREAT for Broadest improveimprove unverisal for TST170 DEX TREAT DEX TREAT for Nextera Nexteraadaboost unverisal signal signal 122 genes 27 genes 30 genes 26 genes 22genes 75 genes 11 genes 24 genes FLG FLG VSIG4 ADAMTS1 ADAMTS1 ARHGEF25CLEC4C LEP KRT5 KRT5 ADAMTS2 ADAMTS2 ADAMTS2 CLEC4C ARHGEF25 PAPPA2 HBG2CLEC4C NES ALOX15B ALOX15B CRH ADAMTS2 KCNA5 NXF3 TEAD4 FAM107A AMPHARHGEF25 CUX2 LEP ADAMTS2 CLEC4C SEMA3G LEP ARHGEF25 CELF4 LEP ARRDC2MYOM3 BPIFB3 ADAMTS1 DAAM2 CELF4 DAAM2 NES SKIL ATP13A3 LAMP5 IGFBP5ARHGEF25 DAAM2 FAM107A PAPPA2 PAPPA2 ARHGEF25 CADM2 HSPA12B TIMP3FAM107A HTRA4 PRG2 VSIG4 ADA CUX2 SLC9A3R2 PRX HSPA12B IGFBP5 ACY3ARRDC4 HTRA4 PACSIN1 PRX ALOX15B HTRA4 KCNA5 ADA CRH NES PTGDR2 TIMP3HSPA12B IGFBP5 KRT5 ADAM17 NES CRH PLD4 ARHGEF25 IGFBP5 KCNA5 LCN6ARFGAP3 ACY3 NOMO1 HTRA4 CLEC4C KRT5 LEP ARRDC2 PLD4 SH3RF2 NES SLC9A3R2LCN6 LRRC26 ARRDC3 SCT ZNF366 TIMP4 ADAMTS1 LEP NES ASTE1 NOX4 SH3PXD2APAPPA2 SEMA3G LRRC26 OLAH ATOH8 PACSIN1 SULT2A1 FAM107A KRT5 NES PRXATP13A3 SERPINF1 FAM219A PRG2 AMPH OLAH PTGDR2 C10orf2 SKIL PPP1R3C AMPHPRG2 PACSIN1 SEMA3G C22orf39 SEMA3G NFE2L1 DAAM2 PAPPA2 PAPPA2 SLC9A3R2CCDC151 TIPARP PODXL LCN6 TEAD4 PRX TIMP3 CD63 LRRC26 HTRA1 ALOX15B CRHPTGDR2 VSIG4 CKAP4 PHEX EMP1 CRH PITPNM3 SEMA3G CLCN1 LILRA4 H19 VSIG4TIMP4 SLC9A3R2 CLEC4M PER1 IGIP LEP PNMT TIMP3 CLIC5 SSUH2 ADAMTS2 ZEB1VSIG4 CNFN C6 ARMS2 APOLD1 CPAMD8 ARHGEF15 PLD4 DDI2 IRF6 CUX2 EBI3 NPR1HTRA4 ELMO3 ALPK3 ENC1 ZCCHC24 ETV3 SAMD4A FAR2 STAG3 FOS RP1L1 FSTL3A1CF GATSL2 MN1 GBP2 CD34 GINS4 MYH14 GSTA3 TENC1 HEATR9 FSTL3 HEG1PRDM16 HIPK2 FMO3 JUN UACA LILRA4 TEK MRPS35 SOX17 MTRNR2L6 FLNC ORAI3TMC7 PARN KIF1C PDE8B CLIC5 PI4KAP1 SYNPO PPP1R17 CACNA1C PSMD11 ERGRGP1 PTPN21 RNF6 NTRK2 SCAMP1 WWTR1 SERPINF1 CYP26B1 SKIL ZEB1 SLC26A2AIF1L SLC4A3 C8B SLIT3 KIF26A SMPD3 ZBTB16 SPDYE5 BCL6B ST6GALNAC3 FKBP5THTPA FN1 TIPARP AQP7 TMEM108 IL1R2 TMEM11 ERRFI1 TNFRSF21 SRPX TPCN1GJA5 TPST1 GPR116 TRAF3IP1 JAG2 TRUB1 MYL2 TTC21A ADCY1 USP54 NRG3 ZMYM6GPR4 PITPNM3 SERPINA3 CPB2 ADRA2C ANO1 CA3 C14orf37 TEAD4 TAT LEAP2 HAO2SEMA3G ADAMTS1 APOLD1 IGFBP5 HSPA12B GATA5 SLC9A3R2 RSPO3 AQP7P1 PRXPNMT MYOM3 HRG TIMP3 ARHGEF25 HTRA4 SCN5A OLAH NES TIMP4 PAPPA2 AZGP1FAM107A PRG2 AMPH AP3B2 KRT17 DAAM2 LCN6 ALOX15B CRH VSIG4 LEP ADAMTS2ARMS2

TABLE 2 DEX Analysis TruSeq Standard Nextera Flex for enrichmentImportance DEX Fold Standard Fold Gene Ranking TREAT ChangeBootstrapping Jackknifing DEX Change Jackknifing ADAMTS1 1 Y 1.79 Y Y Y+3.2 Y ADAMTS2 1 Y 3.61 Y Y Y +12.2  Y ALOX15B 1 Y 2.51 Y Y Y +5.3 YARHGEF25 1 Y 2.02 Y Y Y +3.8 Y DAAM2 1 Y 2.48 Y Y Y +5.4 Y FAM107A 1 Y2.31 Y Y Y +4.3 Y HTRA4 1 Y 2.03 Y Y Y +4.0 Y IGFBP5 1 Y 1.81 Y Y Y +3.4Y KRT5 1 Y −2.52 Y Y Y −4.8 Y LEP 1 Y 3.48 Y Y Y +8.1 Y NES 1 Y 2.15 Y YY +4.2 Y PRX 1 Y 1.93 Y Y Y +3.3 Y SEMA3G 1 Y 1.78 Y Y Y +3.5 Y SLC9A3R21 Y 1.85 Y Y Y +3.4 Y TIMP3 1 Y 2.01 Y Y Y +3.7 Y VSIG4 1 Y 3.03 Y Y Y+8.2 Y PAPPA2 2 Y 2.20 Y Y Y +4.2 N AMPH 2 Y 2.37 Y Y Y +4.1 N HSPA12B 2Y 1.82 Y Y Y +3.2 N PTGDR2 2 Y −1.67 N N Y −3.5 Y LCN6 2 Y 2.49 Y N Y+4.4 Y OLAH 2 Y 2.07 N N Y +5.2 Y APOLD1 3 Y 1.80 N Y N NA N CUX2 3 Y−1.73 N Y N NA N PITPNM3 3 Y 1.66 N Y N NA N PLD4 3 Y −1.59 N Y N NA NPNMT 3 Y 1.98 N Y N NA N CLEC4C 3 Y −1.86 Y Y N NA N CRH 3 Y 2.54 Y Y NNA N PRG2 3 Y 2.36 Y Y N NA N TEAD4 3 Y 1.74 Y Y N NA N TIMP4 3 Y 2.17 YY N NA N CELF4 3 N NA N N Y +5.3 Y KCNA5 3 N NA N N Y −4.0 Y LRRC26 3 NNA N N Y −4.4 Y ARMS2 4 Y 4.43 Y N N NA N FLG 4 Y −3.05 Y N N NA NPACSIN1 4 Y −1.70 N N Y −3.4 N A1CF 5 Y 1.37 N N N NA N ADCY1 5 Y 1.62 NN N NA N ADRA2C 5 Y 1.69 N N N NA N AIF1L 5 Y 1.48 N N N NA N ALPK3 5 Y1.35 N N N NA N ANO1 5 Y 1.69 N N N NA N AP3B2 5 Y 2.40 N N N NA N AQP75 Y 1.51 N N N NA N AQP7P1 5 Y 1.88 N N N NA N ARHGEF15 5 Y 1.33 N N NNA N AZGP1 5 Y 2.27 N N N NA N BCL6B 5 Y 1.50 N N N NA N BPIFB3 5 Y−1.80 N N N NA N C14orf37 5 Y 1.73 N N N NA N C6 5 Y 1.33 N N N NA N C8B5 Y 1.49 N N N NA N CA3 5 Y 1.72 N N N NA N CACNA1C 5 Y 1.42 N N N NA NCADM2 5 Y −1.76 N N N NA N CD34 5 Y 1.37 N N N NA N CLIC5 5 Y 1.41 N N NNA N CPB2 5 Y 1.69 N N N NA N CYP26B1 5 Y 1.48 N N N NA N EMP1 5 Y 1.30N N N NA N ERG 5 Y 1.43 N N N NA N ERRFI1 5 Y 1.54 N N N NA N FAM219A 5Y 1.24 N N N NA N FKBP5 5 Y 1.50 N N N NA N FLNC 5 Y 1.40 N N N NA NFMO3 5 Y 1.39 N N N NA N FN1 5 Y 1.51 N N N NA N FSTL3 5 Y 1.38 N N N NAN GATA5 5 Y 1.82 N N N NA N GJA5 5 Y 1.55 N N N NA N GPR116 5 Y 1.56 N NN NA N GPR4 5 Y 1.65 N N N NA N H19 5 Y 1.32 N N N NA N HAO2 5 Y 1.75 NN N NA N HBG2 5 Y −2.15 N N N NA N HRG 5 Y 1.99 N N N NA N HTRA1 5 Y1.29 N N N NA N IGIP 5 Y 1.32 N N N NA N IL1R2 5 Y 1.53 N N N NA N IRF65 Y 1.34 N N N NA N JAG2 5 Y 1.57 N N N NA N KIF1C 5 Y 1.41 N N N NA NKIF26A 5 Y 1.49 N N N NA N KRT17 5 Y 2.47 N N N NA N LAMP5 5 Y −1.77 N NN NA N LEAP2 5 Y 1.74 N N N NA N MN1 5 Y 1.37 N N N NA N MYH14 5 Y 1.38N N N NA N MYL2 5 Y 1.60 N N N NA N MYOM3 5 Y 1.99 N N N NA N NFE2L1 5 Y1.26 N N N NA N NOMO1 5 Y −1.49 N N N NA N NPR1 5 Y 1.34 N N N NA N NRG35 Y 1.62 N N N NA N NTRK2 5 Y 1.45 N N N NA N NXF3 5 Y −1.96 N N N NA NPODXL 5 Y 1.27 N N N NA N PPP1R3C 5 Y 1.25 N N N NA N PRDM16 5 Y 1.38 NN N NA N PTPN21 5 Y 1.44 N N N NA N RP1L1 5 Y 1.36 N N N NA N RSPO3 5 Y1.87 N N N NA N SAMD4A 5 Y 1.35 N N N NA N SCN5A 5 Y 2.03 N N N NA NSERPINA3 5 Y 1.67 N N N NA N SH3PXD2A 5 Y 1.23 N N N NA N SH3RF2 5 Y1.18 N N N NA N SOX17 5 Y 1.40 N N N NA N SRPX 5 Y 1.55 N N N NA N SSUH25 Y 1.33 N N N NA N STAG3 5 Y 1.36 N N N NA N SULT2A1 5 Y 1.23 N N N NAN SYNPO 5 Y 1.42 N N N NA N TAT 5 Y 1.74 N N N NA N TEK 5 Y 1.39 N N NNA N TENC1 5 Y 1.38 N N N NA N TMC7 5 Y 1.40 N N N NA N UACA 5 Y 1.39 NN N NA N WWTR1 5 Y 1.47 N N N NA N ZBTB16 5 Y 1.50 N N N NA N ZCCHC24 5Y 1.35 N N N NA N ZEB1 5 Y 1.48 N Y N NA N ZNF366 5 Y 1.23 N N N NA N

TABLE 3 Adaboost Analysis Fold Change in Frequency Used By AverageAdaBoost Model Importance Preeclampsia Adaboost Importance RankingTruSeq Nextera Flex TruSeq Nextera Flex TruSeq Nextera Flex ADAMTS2 1+11.6  +12.2  100%  100% 9% 8% ARHGEF25 1 +4.1 +3.8 100%  100% 11%  5%CRH 1 +5.7 +3.9 14% 100% 2% 4% LEP 1 +10.7  +8.1 100%  100% 8% 17%  NES1 +4.5 +4.2  7% 100% 4% 4% PAPPA2 1 +4.9 +4.2 64% 100% 3% 8% SKIL 1 +1.5+1.4 86%  78% 3% 3% ACY3 2 ND −2.3 ND 100% ND 3% ADA 2 ND −1.6 ND 100%ND 5% ARRDC2 2 +1.8 ND 93% ND 3% ND ATP13A3 2 ND +1.5 ND 100% ND 5%CLEC4C 2 −3.6 ND 100%  ND 18%  ND HTRA4 2 ND +4.0 ND 100% ND 5% KCNA5 2ND −4.0 ND 100% ND 8% MYOM3 2 ND +4.2 ND 100% ND 7% NOX4 2 ND −1.8 ND100% ND 2% PACSIN1 2 ND −3.4 ND 100% ND 2% PLD4 2 ND −2.7 ND 100% ND 3%SCT 2 ND −3.3 ND 100% ND 3% SERPINF1 2 ND −1.6 ND 100% ND 2% VSIG4 3+8.1 ND 43% ND 3% ND ARRDC4 3 +2.0 ND 36% ND 4% ND LILRA4 3 ND −2.7 ND 33% ND 1% LRRC26 3 ND −4.4 ND  56% ND 2% PER1 3 ND +2.2 ND  33% ND 1%PHEX 3 ND −2.2 ND  56% ND 2% SEMA3G 3 ND +3.5 ND  67% ND 5% TIPARP 3 ND+1.2 ND  67% ND 2% ADAM17 4 ND ND ND ND ND ND ARFGAP3 4 ND ND ND ND NDND ARRDC3 4 ND ND ND ND ND ND ASTE1 4 ND ND ND ND ND ND ATOH8 4 ND ND NDND ND ND C10orf2 4 ND ND ND ND ND ND C22orf39 4 ND ND ND ND ND NDCCDC151 4 ND ND ND ND ND ND CD63 4 ND ND ND ND ND ND CKAP4 4 ND ND ND NDND ND CLCN1 4 ND ND ND ND ND ND CLEC4M 4 ND ND ND ND ND ND CLIC5 4 ND NDND ND ND ND CNFN 4 ND ND ND ND ND ND CPAMD8 4 ND ND ND ND ND ND CUX2 4ND ND ND ND ND ND DDI2 4 ND ND ND ND ND ND EBI3 4 ND ND ND ND ND NDELMO3 4 ND ND ND ND ND ND ENC1 4 ND ND ND ND ND ND ETV3 4 ND ND ND ND NDND FAR2 4 ND ND ND ND ND ND FOS 4 ND ND ND ND ND ND FSTL3 4 ND ND ND NDND ND GATSL2 4 ND ND ND ND ND ND GBP2 4 ND ND ND ND ND ND GINS4 4 ND NDND ND ND ND GSTA3 4 ND ND ND ND ND ND HEATR9 4 ND ND ND ND ND ND HEG1 4ND ND ND ND ND ND HIPK2 4 ND ND ND ND ND ND JUN 4 ND ND ND ND ND NDMRPS35 4 ND ND ND ND ND ND MTRNR2L6 4 ND ND ND ND ND ND ORAI3 4 ND ND NDND ND ND PARN 4 ND ND ND ND ND ND PDE8B 4 ND ND ND ND ND ND PI4KAP1 4 NDND ND ND ND ND PPP1R17 4 ND ND ND ND ND ND PRG2 4 ND ND ND ND ND NDPSMD11 4 ND ND ND ND ND ND RGP1 4 ND ND ND ND ND ND RNF6 4 ND ND ND NDND ND SCAMP1 4 ND ND ND ND ND ND SLC26A2 4 ND ND ND ND ND ND SLC4A3 4 NDND ND ND ND ND SLIT3 4 ND ND ND ND ND ND SMPD3 4 ND ND ND ND ND NDSPDYE5 4 ND ND ND ND ND ND ST6GALNAC3 4 ND ND ND ND ND ND THTPA 4 ND NDND ND ND ND TMEM108 4 ND ND ND ND ND ND TMEM11 4 ND ND ND ND ND NDTNFRSF21 4 ND ND ND ND ND ND TPCN1 4 ND ND ND ND ND ND TPST1 4 ND ND NDND ND ND TRAF3IP1 4 ND ND ND ND ND ND TRUB1 4 ND ND ND ND ND ND TTC21A 4ND ND ND ND ND ND USP54 4 ND ND ND ND ND ND ZMYM6 4 ND ND ND ND ND ND

TABLE 4 Gene Glossary Gene Symbol Used Official Gene in Patent SymbolApproved Name HGNC ID Location KRT5 KRT5 keratin 5 HGNC:6442 12q13.13CUX2 CUX2 cut like homeobox 2 HGNC:19347 12q24.11-q24.12 CLEC4C CLEC4CC-type lectin domain family 4 member C HGNC:13258 12p13.31 PLD4 PLD4phospholipase D family member 4 HGNC:23792 14q32.33 ALOX15B ALOX15Barachidonate 15-lipoxygenase type B HGNC:434 17p13.1 PRG2 PRG2proteoglycan 2, pro eosinophil major basic protein HGNC:9362 11q12.1HTRA4 HTRA4 HtrA serine peptidase 4 HGNC:26909 8p11.22 AMPH AMPHamphiphysin HGNC:471 7p14.1 PNMT PNMT phenylethanolamineN-methyltransferase HGNC:9160 17q12 LEP LEP leptin HGNC:6553 7q32.1PAPPA2 PAPPA2 pappalysin 2 HGNC:14615 1q25.2 CRH CRH corticotropinreleasing hormone HGNC:2355 8q13.1 TIMP4 TIMP4 TIMP metallopeptidaseinhibitor 4 HGNC:11823 3p25.2 APOLD1 APOLD1 apolipoprotein L domaincontaining 1 HGNC:25268 12p13.1 ARHGEF25 ARHGEF25 Rho guanine nucleotideexchange factor 25 HGNC:30275 12q13.3 TIMP3 TIMP3 TIMP metallopeptidaseinhibitor 3 HGNC:11822 22q12.3 SEMA3G SEMA3G semaphorin 3G HGNC:304003p21.1 IGFBP5 IGFBP5 insulin like growth factor binding protein 5HGNC:5474 2q35 PRX PRX periaxin HGNC:13797 19q13.2 PITPNM3 PITPNM3PITPNM family member 3 HGNC:21043 17p13.2-p13.1 FAM107A FAM107A familywith sequence similarity 107 member A HGNC:30827 3p14.3-p14.2 TEAD4TEAD4 TEA domain transcription factor 4 HGNC:11717 12p13.33 HSPA12BHSPA12B heat shock protein family A (Hsp70) member 12B HGNC:16193 20p13NES NES nestin HGNC:7756 1q23.1 SLC9A3R2 SLC9A3R2 SLC9A3 regulator 2HGNC:11076 16p13.3 ZEB1 ZEB1 zinc finger E-box binding homeobox 1HGNC:11642 10p11.22 ADAMTS1 ADAMTS1 ADAM metallopeptidase withthrombospondin type 1 motif 1 HGNC:217 21q21.3 DAAM2 DAAM2 dishevelledassociated activator of morphogenesis 2 HGNC:18143 6p21.2 ADAMTS2ADAMTS2 ADAM metallopeptidase with thrombospondin type 1 motif 2HGNC:218 5q35.3 VSIG4 VSIG4 V-set and immunoglobulin domain containing 4HGNC:17032 Xq12 ARRDC2 ARRDC2 arrestin domain containing 2 HGNC:2522519p13.11 SKIL SKIL SKI like proto-oncogene HGNC:10897 3q26.2 ARRDC4ARRDC4 arrestin domain containing 4 HGNC:28087 15q26.2 KCNA5 KCNA5potassium voltage-gated channel subfamily A member 5 HGNC:6224 12p13.32MYOM3 MYOM3 myomesin 3 HGNC:26679 1p36.11 ATP13A3 ATP13A3 ATPase 13A3HGNC:24113 3q29 ADA ADA adenosine deaminase HGNC:186 20q13.12 ACY3 ACY3aminoacylase 3 HGNC:24104 11q13.2 SCT SCT secretin HGNC:10607 11p15.5NOX4 NOX4 NADPH oxidase 4 HGNC:7891 11q14.3 PACSIN1 PACSIN1 proteinkinase C and casein kinase substrate in neurons 1 HGNC:8570 6p21.3SERPINF1 SERPINF1 serpin family F member 1 HGNC:8824 17p13.3 TIPARPTIPARP TCDD inducible poly(ADP-ribose) polymerase HGNC:23696 3q25.31LRRC26 LRRC26 leucine rich repeat containing 26 HGNC:31409 9q34.3 PHEXPHEX phosphate regulating endopeptidase homolog X-linked HGNC:8918Xp22.11 LILRA4 LILRA4 leukocyte immunoglobulin like receptor A4HGNC:15503 19q13.42 PER1 PER1 period circadian regulator 1 HGNC:884517p13.1 CELF4 CELF4 CUGBP Elav-like family member 4 HGNC:14015 18q12.2LCN6 LCN6 lipocalin 6 HGNC:17337 9q34.3 OLAH OLAH oleoyl-ACP hydrolaseHGNC:25625 10p13 PTGDR2 PTGDR2 prostaglandin D2 receptor 2 HGNC:450211q12.2 JUN JUN Jun proto-oncogene, AP-1 transcription factor subunitHGNC:6204 1p32.1 PDE8B PDE8B phosphodiesterase 8B HGNC:8794 5q13.3 GSTA3GSTA3 glutathione S-transferase alpha 3 HGNC:4628 6p12.2 RGP1 RGP1 RGP1homolog, RAB6A GEF complex partner 1 HGNC:21965 9p13.3 USP54 USP54ubiquitin specific peptidase 54 HGNC:23513 10q22.2 MRPS35 MRPS35mitochondrial ribosomal protein S35 HGNC:16635 12p11.22 HEATR9 HEATR9HEAT repeat containing 9 HGNC:26548 17q12 FSTL3 FSTL3 follistatin like 3HGNC:3973 19p13.3 DDI2 DDI2 DNA damage inducible 1 homolog 2 HGNC:245781p36.21 ZMYM6 ZMYM6 zinc finger MYM-type containing 6 HGNC:13050 1p34.3ST6GALNAC3 ST6GALNAC3 ST6 N-acetylgalactosaminidealpha-2,6-sialyltransferase 3 HGNC:19343 1p31.1 GBP2 GBP2 guanylatebinding protein 2 HGNC:4183 1p22.2 ETV3 ETV3 ETS variant 3 HGNC:34921q23.1 ADAM17 ADAM17 ADAM metallopeptidase domain 17 HGNC:195 2p25.1ATOH8 ATOH8 atonal bHLH transcription factor 8 HGNC:24126 2p11.2 SLC4A3SLC4A3 solute carrier family 4 member 3 HGNC:11029 2q35 TRAF3IP1TRAF3IP1 TRAF3 interacting protein 1 HGNC:17861 2q37.3 TTC21A TTC21Atetratricopeptide repeat domain 21A HGNC:30761 3p22.2 HEG1 HEG1 heartdevelopment protein with EGF like domains 1 HGNC:29227 3q21.2 ASTE1ASTE1 asteroid homolog 1 HGNC:25021 3q22.1 TMEM108 TMEM108 transmembraneprotein 108 HGNC:28451 3q22.1 ENC1 ENC1 ectodermal-neural cortex 1HGNC:3345 5q13.3 SCAMP1 SCAMP1 secretory carrier membrane protein 1HGNC:10563 5q14.1 ARRDC3 ARRDC3 arrestin domain containing 3 HGNC:292635q14.3 SLC26A2 SLC26A2 solute carrier family 26 member 2 HGNC:10994 5q32SLIT3 SLIT3 slit guidance ligand 3 HGNC:11087 5q34-q35.1 CLIC5 CLIC5chloride intracellular channel 5 HGNC:13517 6p21.1 TNFRSF21 TNFRSF21 TNFreceptor superfamily member 21 HGNC:13469 6p12.3 PPP1R17 PPP1R17 proteinphosphatase 1 regulatory subunit 17 HGNC:16973 7p14.3 TPST1 TPST1tyrosylprotein sulfotransferase 1 HGNC:12020 7q11.21 GATSL2 CASTOR2cytosolic arginine sensor for mTORC1 subunit 2 HGNC:37073 7q11.23 SPDYE5SPDYE5 speedy/RINGO cell cycle regulator family member E5 HGNC:354647q11.23 HIPK2 HIPK2 homeodomain interacting protein kinase 2 HGNC:144027q34 MTRNR2L6 MTRNR2L6 MT-RNR2 like 6 HGNC:37163 7q34 CLCN1 CLCN1chloride voltage-gated channel 1 HGNC:2019 7q34 GINS4 GINS4 GINS complexsubunit 4 HGNC:28226 8p11.21 C10orf2 TWNK twinkle mtDNA helicaseHGNC:1160 10q24.31 TRUB1 TRUB1 TruB pseudouridine synthase family member1 HGNC:16060 10q25.3 FAR2 FAR2 fatty acyl-CoA reductase 2 HGNC:2553112p11.22 CD63 CD63 CD63 molecule HGNC:1692 12q13.2 CKAP4 CKAP4cytoskeleton associated protein 4 HGNC:16991 12q23.3 TPCN1 TPCN1 twopore segment channel 1 HGNC:18182 12q24.13 RNF6 RNF6 ring finger protein6 HGNC:10069 13q12.13 THTPA THTPA thiamine triphosphatase HGNC:1898714q11.2 FOS FOS Fos proto-oncogene, AP-1 transcription factor subunitHGNC:3796 14q24.3 PARN PARN poly(A)-specific ribonuclease HGNC:860916p13.12 ORAI3 ORAI3 ORAI calcium release-activated calcium modulator 3HGNC:28185 16p11.2 ELMO3 ELMO3 engulfment and cell motility 3 HGNC:1728916q22.1 SMPD3 SMPD3 sphingomyelin phosphodiesterase 3 HGNC:14240 16q22.1TMEM11 TMEM11 transmembrane protein 11 HGNC:16823 17p11.1 PSMD11 PSMD11proteasome 26S subunit, non-ATPase 11 HGNC:9556 17q11.2 EBI3 EBI3Epstein-Barr virus induced 3 HGNC:3129 19p13.3 CLEC4M CLEC4M C-typelectin domain family 4 member M HGNC:13523 19p13.2 CCDC151 CCDC151coiled-coil domain containing 151 HGNC:28303 19p13.2 CPAMD8 CPAMD8 C3and PZP like alpha-2-macroglobulin domain containing 8 HGNC:2322819p13.11 CNFN CNFN cornifelin HGNC:30183 19q13.2 C22orf39 C22orf39chromosome 22 open reading frame 39 HGNC:27012 22q11.21 PI4KAP1 PI4KAP1phosphatidylinositol 4-kinase alpha pseudogene 1 HGNC:33576 22q11.21ARFGAP3 ARFGAP3 ADP ribosylation factor GTPase activating protein 3HGNC:661 22q13.2 FLG FLG filaggrin HGNC:3748 1q21.3 ARMS2 ARMS2age-related maculopathy susceptibility 2 HGNC:32685 10q26.13 CYP26B1CYP26B1 cytochrome P450 family 26 subfamily B member 1 HGNC:20581 2p13.2IRF6 IRF6 interferon regulatory factor 6 HGNC:6121 1q32.2 MYH14 MYH14myosin heavy chain 14 HGNC:23212 19q13.33 PODXL PODXL podocalyxin likeHGNC:9171 7q32.3 PPP1R3C PPP1R3C protein phosphatase 1 regulatorysubunit 3C HGNC:9293 10q23.32 SH3RF2 SH3RF2 SH3 domain containing ringfinger 2 HGNC:26299 5q32 TMC7 TMC7 transmembrane channel like 7HGNC:23000 16p12.3 ZNF366 ZNF366 zinc finger protein 366 HGNC:183165q13.1 ADCY1 ADCY1 adenylate cyclase 1 HGNC:232 7p12.3 C6 C6 complementC6 HGNC:1339 5p13.1 FAM219A FAM219A family with sequence similarity 219member A HGNC:19920 9p13.3 HAO2 HAO2 hydroxyacid oxidase 2 HGNC:48101p12 IGIP IGIP IgA inducing protein HGNC:33847 5q31.3 IL1R2 IL1R2interleukin 1 receptor type 2 HGNC:5994 2q11.2 NTRK2 NTRK2 neurotrophicreceptor tyrosine kinase 2 HGNC:8032 9q21.33 SH3PXD2A SH3PXD2A SH3 andPX domains 2A HGNC:23664 10q24.33 SSUH2 SSUH2 ssu-2 homolog HGNC:248093p25.3 SULT2A1 SULT2A1 sulfotransferase family 2A member 1 HGNC:1145819q13.33 FMO3 FMO3 flavin containing dimethylaniline monoxygenase 3HGNC:3771 1q24.3 GATA5 GATA5 GATA binding protein 5 HGNC:15802 20q13.33HTRA1 HTRA1 HtrA serine peptidase 1 HGNC:9476 10q26.13 C8B C8Bcomplement C8 beta chain HGNC:1353 1p32.2 H19 H19 H19 imprintedmaternally expressed transcript HGNC:4713 11p15.5 MN1 MN1 MN1proto-oncogene, transcriptional regulator HGNC:7180 22q12.1 NFE2L1NFE2L1 nuclear factor, erythroid 2 like 1 HGNC:7781 17q21.3 PRDM16PRDM16 PR/SET domain 16 HGNC:14000 1p36.32 AP3B2 AP3B2 adaptor relatedprotein complex 3 subunit beta 2 HGNC:567 15q25.2 EMP1 EMP1 epithelialmembrane protein 1 HGNC:3333 12p13.1 FLNC FLNC filamin C HGNC:37567q32.1 STAG3 STAG3 stromal antigen 3 HGNC:11356 7q22.1 CPB2 CPB2carboxypeptidase B2 HGNC:2300 13q14.13 TENC1 TNS2 tensin 2 HGNC:1973712q13.13 RP1L1 RP1L1 RP1 like 1 HGNC:15946 8p23.1 A1CF A1CF APOBEC1complementation factor HGNC:24086 10q11.23 NPR1 NPR1 natriuretic peptidereceptor 1 HGNC:7943 1q21.3 TEK TEK TEK receptor tyrosine kinaseHGNC:11724 9p21.2 ERRFI1 ERRFI1 ERBB receptor feedback inhibitor 1HGNC:18185 1p36.23 ARHGEF15 ARHGEF15 Rho guanine nucleotide exchangefactor 15 HGNC:15590 17p13.1 CD34 CD34 CD34 molecule HGNC:1662 1q32.2RSPO3 RSPO3 R-spondin 3 HGNC:20866 6q22.33 ALPK3 ALPK3 alpha kinase 3HGNC:17574 15q25.3 SAMD4A SAMD4A sterile alpha motif domain containing4A HGNC:23023 14q22.2 ZCCHC24 ZCCHC24 zinc finger CCHC-type containing24 HGNC:26911 10q22.3 LEAP2 LEAP2 liver enriched antimicrobial peptide 2HGNC:29571 5q31.1 MYL2 MYL2 myosin light chain 2 HGNC:7583 12q24.11 NRG3NRG3 neuregulin 3 HGNC:7999 10q23.1 ZBTB16 ZBTB16 zinc finger and BTBdomain containing 16 HGNC:12930 11q23.2 SERPINA3 SERPINA3 serpin familyA member 3 HGNC:16 14q32.13 AQP7 AQP7 aquaporin 7 HGNC:640 9p13.3 SRPXSRPX sushi repeat containing protein X-linked HGNC:11309 Xp11.4 UACAUACA uveal autoantigen with coiled-coil domains and ankyrin repeatsHGNC:15947 15q23 ANO1 ANO1 anoctamin 1 HGNC:21625 11q13.3 FKBP5 FKBP5FKBP prolyl isomerase 5 HGNC:3721 6p21.31 SCN5A SCN5A sodiumvoltage-gated channel alpha subunit 5 HGNC:10593 3p22.2 PTPN21 PTPN21protein tyrosine phosphatase non-receptor type 21 HGNC:9651 14q31.3CACNA1C CACNA1C calcium voltage-gated channel subunit alpha1 C HGNC:139012p13.33 ERG ERG ETS transcription factor ERG HGNC:3446 21q22.2 SOX17SOX17 SRY-box 17 HGNC:18122 8q11.23 WWTR1 WWTR1 WW domain containingtranscription regulator 1 HGNC:24042 3q25.1 AIF1L AIF1L allograftinflammatory factor 1 like HGNC:28904 9q34.12-q34.13 CA3 CA3 carbonicanhydrase 3 HGNC:1374 8q21.2 HRG HRG histidine rich glycoproteinHGNC:5181 3q27.3 TAT TAT tyrosine aminotransferase HGNC:11573 16q22.2AQP7P1 AQP7P1 aquaporin 7 pseudogene 1 HGNC:32048 9q13 ADRA2C ADRA2Cadrenoceptor alpha 2C HGNC:283 4p16.3 SYNPO SYNPO synaptopodinHGNC:30672 5q33.1 FN1 FN1 fibronectin 1 HGNC:3778 2q35 GPR116 ADGRF5adhesion G protein-coupled receptor F5 HGNC:19030 6p12.3 KRT17 KRT17keratin 17 HGNC:6427 17q21.2 AZGP1 AZGP1 alpha-2-glycoprotein 1,zinc-binding HGNC:910 7q22.1 BCL6B BCL6B BCL6B transcription repressorHGNC:1002 17p13.1 KIF1C KIF1C kinesin family member 1C HGNC:6317 17p13.2GPR4 GPR4 G protein-coupled receptor 4 HGNC:4497 19q13.32 GJA5 GJA5 gapjunction protein alpha 5 HGNC:4279 1q21.2 C14orf37 ARMH4 armadillo likehelical domain containing 4 HGNC:19846 14q23.1 JAG2 JAG2 jaggedcanonical Notch ligand 2 HGNC:6189 14q32.33 KIF26A KIF26A kinesin familymember 26A HGNC:20226 14q32.33 HBG2 HBG2 hemoglobin subunit gamma 2HGNC:4832 11p15.4 CADM2 CADM2 cell adhesion molecule 2 HGNC:29849 3p12.1LAMP5 LAMP5 lysosomal associated membrane protein family member 5HGNC:16097 20p12.2 NOMO1 NOMO1 NODAL modulator 1 HGNC:30060 16p13.11NXF3 NXF3 nuclear RNA export factor 3 HGNC:8073 Xq22.1 BPIFB3 BPIFB3 BPIfold containing family B member 3 HGNC:16178 20q11.21 CGB8 CGB8chorionic gonadotropin subunit beta 8 HGNC:16453 19q13.33 CGB5 CGB5chorionic gonadotropin subunit beta 5 HGNC:16452 19q13.33 ZSCAN23ZSCAN23 zinc finger and SCAN domain containing 23 HGNC:21193 6p22.1HSPA1A HSPA1A heat shock protein family A (Hsp70) member 1A HGNC:52326p21.33 PMAIP1 PMAIP1 phorbol-12-myristate-13-acetate-induced protein 1HGNC:9108 18q21.32 C8orf4 TCIM transcriptional and immune responseregulator HGNC:1357 8p11.21 ITM2B ITM2B integral membrane protein 2BHGNC:6174 13q14.2 IFIT2 IFIT2 interferon induced protein withtetratricopeptide repeats 2 HGNC:5409 10q23.31 CD74 CD74 CD74 moleculeHGNC:1697 5q33.1 HSPA6 HSPA6 heat shock protein family A (Hsp70) member6 HGNC:5239 1q23.3 TFAP2A TFAP2A transcription factor AP-2 alphaHGNC:11742 6p24.3 TRPV6 TRPV6 transient receptor potential cationchannel subfamily V member 6 HGNC:14006 7q34 EXPH5 EXPH5 exophilin 5HGNC:30578 11q22.3 CAPN6 CAPN6 calpain 6 HGNC:1483 Xq23 ALDH3B2 ALDH3B2aldehyde dehydrogenase 3 family member B2 HGNC:411 11q13.2 RAB3B RAB3BRAB3B, member RAS oncogene family HGNC:9778 1p32.3 MUC15 MUC15 mucin 15,cell surface associated HGNC:14956 11p14.3 GRHL2 GRHL2 grainyhead liketranscription factor 2 HGNC:2799 8q22.3 CSHL1 CSHL1 chorionicsomatomammotropin hormone like 1 HGNC:2442 17q23.3 CSH2 CSH2 chorionicsomatomammotropin hormone 2 HGNC:2441 17q23.3 KISS1 KISS1 KiSS-1metastasis suppressor HGNC:6341 1q32.1 CGA CGA glycoprotein hormones,alpha polypeptide HGNC:1885 6q14.3 PLAC4 PLAC4 p1acenta enriched 4HGNC:14616 21q22.2 PSG1 PSG1 pregnancy specific beta-1-glycoprotein 1HGNC:9514 19q13.2 GH2 GH2 growth hormone 2 HGNC:4262 17q23.3 PSG3 PSG3pregnancy specific beta-1-glycoprotein 3 HGNC:9520 19q13.2 PSG4 PSG4pregnancy specific beta-1-glycoprotein 4 HGNC:9521 19q13.31 PSG7 PSG7pregnancy specific beta-1-glycoprotein 7 (gene/pseudogene) HGNC:952419q13.31 PSG11 PSG11 pregnancy specific beta-1-glycoprotein 11 HGNC:951619q13.31 CSH1 CSH1 chorionic somatomammotropin hormone 1 HGNC:244017q23.3 PSG2 PSG2 pregnancy specific beta-1-glycoprotein 2 HGNC:951919q13.31 hydroxy-delta-5-steroid dehydrogenase, HSD3B1 HSD3B1 3 beta-and steroid delta-isomerase 1 HGNC:5217 1p12 LGALS14 LGALS14 galectin 14HGNC:30054 19q13.2 FCGR1C FCGR1CP Fc fragment of IgG receptor Ic,pseudogene HGNC:3615 1q21.1 PSG5 PSG5 pregnancy specificbeta-l-glycoprotein 5 HGNC:9522 19q13.31 LAGALS13 LGALS13 galectin 13HGNC:15449 19q13.2 GCM1 GCM1 glial cells missing transcription factor 1HGNC:4197 6p12.1

The term “plurality” refers to more than one element. For example, theterm is used herein in reference to a number of C-RNA molecules thatserve as a signature indicative of preeclampsia.

A plurality may include any two, any three, any four, any five, any six,any seven, any eight, any nine, any ten, any eleven, any twelve, anythirteen, any fourteen, any fifteen, any sixteen, any seventeen, anyeighteen, any nineteen, any twenty, any twenty-one, any twenty-two, anytwenty-three, any twenty-four, any twenty-five, any twenty-six, anytwenty-seven, any twenty-eight, any twenty-nine, any thirty, anythirty-one, any thirty-two, any thirty-three, any thirty-four, anythirty-five, any thirty-six, any thirty-seven, any thirty-eight, anythirty-nine, any forty, any forty-one, any forty-two, any forty-three,any forty-four, any forty-five, any forty-six, any forty-seven, anyforty-eight, any forty-nine, any fifty, any fifty-one, any fifty-two,any fifty-three, any fifty-four, any fifty-five, any fifty-six, anyfifty-seven, any fifty-eight, any fifty-nine, any sixty, any sixty-one,any sixty-two, any sixty-three, any sixty-four, any sixty-five, anysixty-six, any sixty-seven, any sixty-eight, any sixty-nine, anyseventy, any seventy-one, any seventy-two, any seventy-three, anyseventy-four, any seventy-five, any seventy-six, any seventy-seven, anyseventy-eight, any seventy-nine, any eighty, any eighty-one, anyeighty-two, any eighty-three, any eighty-four, any eighty-five, anyeighty-six, any eighty-seven, any eighty-eight, any eighty-nine, anyninety, any ninety-one, any ninety-two, any ninety-three, anyninety-four, any ninety-five, any ninety-six, any ninety-seven, anyninety-eight, any ninety-nine, any one hundred, any one hundred and one,any one hundred and two, any one hundred and three, any one hundred andfour, any one hundred and five, any one hundred and six, any one hundredand seven, any one hundred and eight, any one hundred and nine, any onehundred ten, any one hundred eleven, any one hundred twelve, any onehundred thirteen, any one hundred fourteen, any one hundred fifteen, anyone hundred sixteen, any one hundred seventeen, any one hundredeighteen, any one hundred nineteen, any one hundred twenty, any onehundred twenty-one, or any one hundred twenty-two of the moleculesrecited in a list described herein. A plurality may include a least anyof the numbers recited above. A plurality may include more than any ofthe numbers recited above. A plurality may include a range of any ofthose recited above. In some embodiments, a C-RNA signature indicativeof preeclampsia includes just one of the biomarkers recited above.

The identification and/or quantification of one of these C-RNAsignatures within a sample obtained from a subject can be used todetermine that the subject suffers from preeclampsia or is at a risk ofdeveloping preeclampsia.

A sample may be a biological sample or biosample, including but notlimited to blood, serum, plasma, sweat, tears, urine, sputum, lymph,saliva, amniotic fluid, a tissue biopsy, swab, or smear, including forexample, but not limited to, a placental tissue sample. In somepreferred embodiments, a biological sample is a cell free plasma sample.A biological sample may be a maternal sample obtained from a pregnantfemale subject.

As used herein, the term “subject” refers to a human subject as well asa non-human mammalian subject. Although the examples herein concernhumans and the language is primarily directed to human concerns, theconcept of this disclosure is applicable to any mammal, and is useful inthe fields of veterinary medicine, animal sciences, researchlaboratories and such.

A subject may be a pregnant female, including a pregnant female in anygestational stages of pregnancy. The gestational stage of pregnancy maybe, for example, the first trimester, the second trimester, includinglate second trimester, or the third trimester, including early thirdtrimester. The gestational stage of pregnancy may be, for example,before 16 weeks of pregnancy, before 20 weeks of pregnancy, or after 20weeks of pregnancy. The gestational stage of pregnancy may be, forexample, 8-18 weeks of pregnancy, 10-14 weeks of pregnancy, 11-14 weeksof pregnancy, 11-13 weeks, or 12-13 weeks of pregnancy.

The discovery of cell-free fetal nucleic acids in maternal plasma hasopened up new possibilities for noninvasive prenatal diagnosis. Over thelast few years, a number of approaches have been demonstrated to allowsuch circulating fetal nucleic acids to be used for the prenataldetection of chromosomal aneuploidies. Any of the methods described forexample in Poon et al., 2000, Clin Chem; 1832-4; Poon et al., 2001, AnnN Y Acad Sci; 945:207-10; Ng et al., 2003, Clin Chem; 49(5):727-31; Nget al., 2003, Proc Natl Acad Sci USA;100(8):4748-53; Tsui et al., 2004,J Med Genet; 41(6):461-7; Go et al., 2004, Clin Chem; 50(8):1413-4;Smets et al., 2006, Clin Chim Acta; 364(1-2):22-32; Tsui et al., 2006,Methods Mol Biol; 336:123-34; Purwosunu et al., 2007, Clin Chem;53(3):399-404; Chim et al., 2008, Clin Chem; 54(3):482-90; Tsui and Lo,2008, Methods Mol Biol; 444:275-89; Lo, 2008, Ann N Y Acad Sci;1137:140-143; Miura et al., 2010, Prenat Diagn; 30(9):849-61; Li et al.,2012, Clin Chim Acta; 413(5-6):568-76; Williams et al., 2013, Proc NatlAcad Sci USA; 110(11):4255-60; Tsui et al., 2014, Clin Chem;60(7):954-62; Tsang et al., 2017, Proc Natl Acad Sci USA;114(37):E7786-E7795, and US Patent Publication US 2014/0243212 may beused in the methods described herein.

The detection and identification of biomarkers of a C-RNA signaturewithin the maternal circulation indicative of preeclampsia or a risk fordeveloping preeclampsia may involve any of a variety of technologies.For example, biomarkers may be detected in serum by radioimmunoassay orthe polymerase chain reaction (PCR) technique may be used.

In various embodiments, the identification of the biomarkers of a C-RNAsignature within the maternal circulation indicative of preeclampsia ora risk for developing preeclampsia may involve sequencing the C-RNAmolecules. Any of a number of sequencing technologies can be utilized,including, but not limited to, any of a variety of high-throughputsequencing techniques.

In some embodiments, the C-RNA population within a maternal biosamplemay be subject to enrichment of RNA sequences the include protein-codingsequences prior to sequencing. Any of a variety of platforms availablefor whole-exome enrichment and sequencing may be used, including but notlimited to the Agilent SureSelect Human All Exon platform (Chen et al.,2015a, Cold Spring Harb Protoc; 2015(7):626-33. doi:10.1101/pdb.prot083659); the Roche NimbleGen SeqCap EZ Exome Library SRplatform (Chen et al., 2015b, Cold Spring Harb Protoc; 2015(7):634-41.doi: 10.1101/pdb.prot084855); or the Illumina TruSeq Exome Enrichmentplatform (Chen et al., 2015c, Cold Spring Harb Protoc; 2015(7):642-8.doi:10.1101/pdb.prot084863). See also “TruSeq™ Exome Enrichment Guide,”Catalog # FC-930-1012 Part #15013230 Rev. B November 2010 and Illumina's“TruSeq™ RNA Sample Preparation Guide,” Catalog #RS-122-9001DOC Part#15026495 Rev. F March 2014.

In particular embodiments, biomarkers of a C-RNA signature within thematernal circulation indicative of preeclampsia or a risk for developingpreeclampsia may be detected and identified using microarray techniques.In this method, polynucleotide sequences of interest are plated, orarrayed, on a microchip substrate. The arrayed sequences are thenhybridized with a maternal biosample, or a purified and/or enrichedportion thereof. Microarrays may include a variety of solid supportsincluding, but not limited to, beads, glass microscope slides, glasswafers, gold, silicon, microchips, and other plastic, metal, ceramic, orbiological surfaces. Microarray analysis can be performed bycommercially available equipment, following manufacturer's protocols,such as by using Illumina's technology.

With obtaining, shipping, storing, and/or processing blood samples forthe preparation of circulating RNA, steps may be taken to stabilize thesample and/or prevent the disruption of cell membranes resulting in therelease of cellular RNAs into the sample. For example, in someembodiments, blood samples may be collected, shipped, and/or stored intubes that have cell- and DNA-stabilizing properties, such as StreckCell-Free DNA BCT® blood collection tubes, prior to processing intoplasma. In some embodiments, blood samples are not exposed to EDTA. See,for example, Qin et al., 2013, BMC Research Notes; 6:380 and Medina Diazet al., 2016, PLoS ONE; 11(11):e0166354.

In some embodiments, blood samples are processed into plasma withinabout 24 to about 72 hours of the blood draw, and in some embodiments,within about 24 hours of the blood draw. In some embodiments, bloodsamples are maintained, stored, and/or shipped at room temperature priorto processing into plasma.

In some embodiments, blood samples are maintained, stored, and/orshipped without exposure to chilling (for example, on ice) or freezingprior to processing into plasma.

The disclosure includes kits for use in the diagnosis of preeclampsiaand the identification of pregnant women at risk for developingpreeclampsia. A kit is any manufacture (e.g. a package or container)including at least one reagent, e.g. a probe, for specifically detectinga C-RNA signature within the maternal circulation as described hereinthat is indicative of preeclampsia or a risk for developingpreeclampsia. The kit may be promoted, distributed, or sold as a unitfor performing the methods of the present disclosure.

The use of signatures of circulating RNA found in the maternalcirculation specific to preeclampsia in noninvasive methods for thediagnosis of preeclampsia and the identification of pregnant women atrisk for developing preeclampsia may be combined with appropriatemonitoring and medical management. For example, further tests may beordered. Such test may include, for example, blood tests to measureliver function, kidney function, and/or platelet and various clottingproteins, urine analysis to measure protein or creatinine levels, fetalultrasound to measure monitor fetal growth, weight, and amniotic fluid,a nonstress test to measure how fetal heart rate with fetal movement,and/or a biophysical profile using ultrasound to measure your fetalbreathing, muscle tone, and movement and the volume of amniotic fluidmay be ordered. Therapeutic interventions may include, for example,increasing the frequency of prenatal visits, antihypertensivemedications to lower blood pressure, corticosteroid medications,anticonvulsant medications, bed rest, hospitalization, and/or earlydelivery. See, for example, Townsend et al., 2016 “Current best practicein the management of hypertensive disorders in pregnancy,” Integr BloodPress Control; 9: 79-94.

Therapeutic interventions may include the administration of low doseaspirin to pregnant women identified at risk of for developingpreeclampsia. A recent multicenter, double-blind, placebo-controlledtrial demonstrated that treatment of women at high risk for pretermpreeclampsia with low-dose aspirin resulted in a lower incidence of thisdiagnosis compared to placebo (Rolnik et al., 2017, “Aspirin versusPlacebo in Pregnancies at High Risk for Preterm Preeclampsia,” N Engl JMed; 377(7):613-622). Dosages of low dose aspirin include, but are notlimited to, about 50 to about 150 mg per day, about 60 to about 80 mgper day, about 100 or more mg per day, or about 150 mg per day.Administration may begin, for example, at or before 16 weeks ofgestation or from 11 to 14 weeks of gestation. Administration maycontinue thru 36 weeks of gestation.

The invention is defined in the claims. However, below is provided anon-exhaustive list of non-limiting embodiments. Any one or more of thefeatures of these embodiments may be combined with any one or morefeatures of another example, embodiment, or aspect described herein.

Embodiment 1 includes a method of detecting preeclampsia and/ordetermining an increased risk for preeclampsia in a pregnant female, themethod comprising:

identifying in a biosample obtained from the pregnant women a pluralityof circulating RNA (C-RNA) molecules;

wherein a plurality of C-RNA molecules selected from:

a plurality of C-RNA molecules encoding at least a portion of a proteinselected from any one or more, any two or more, any three or more, anyfour or more, any five or more, any six or more, any seven or more, anyeight or more, any nine or more, any ten or more, any eleven or more,any twelve, any thirteen or more, any fourteen or more, any fifteen ormore, any sixteen or more, any seventeen or more, any eighteen or more,any nineteen or more, any twenty or more, any twenty one or more, anytwenty two or more, any twenty three or more, any twenty four or more,any twenty five or more, any twenty-six or more, any twenty-seven ormore, any twenty-eight or more, any twenty-nine or more, any thirty ormore, any thirty-one or more, any thirty-two or more, any thirty-threeor more, any thirty-four or more, any thirty-five or more, anythirty-six or more, any thirty-seven or more, any thirty-eight or more,any thirty-nine or more, any forty or more, any forty-one or more, anyforty-two or more, any forty-three or more, any forty-four or more, anyforty-five or more, any forty-six or more, any forty-seven or more, anyforty-eight or more, or all forth-nine of those listed in Table S9 ofExample 7; or

a plurality of C-RNA molecules encoding at least a portion of a proteinselected from any one or more, any two or more, any three or more, anyfour or more, any five or more, any six or more, any seven or more, anyeight or more, any nine or more, any ten or more, any eleven or more,any twelve or more, or all thirteen of AKAP2, ARRB1, CPSF7, INO80C,JAG1, MSMP, NR4A2, PLEK, RAP1GAP2, SPEG, TRPS1, UBE2Q1, and ZNF768

is indicative of preeclampsia and/or an increased risk for preeclampsiain the pregnant women.

Embodiment 2 includes a method of detecting preeclampsia and/ordetermining an increased risk for preeclampsia in a pregnant female, themethod comprising:

obtaining a biosample from the pregnant female;

purifying a population of circulating RNA (C-RNA) molecules from thebiosample;

identifying protein coding sequences encoded by the C-RNA moleculeswithin the purified population of C-RNA molecules;

wherein the protein coding sequences encoded by the C-RNA moleculesencoding at least a portion of a protein are selected from:

any one or more, any two or more, any three or more, any four or more,any five or more, any six or more, any seven or more, any eight or more,any nine or more, any ten or more, any eleven or more, any twelve, anythirteen or more, any fourteen or more, any fifteen or more, any sixteenor more, any seventeen or more, any eighteen or more, any nineteen ormore, any twenty or more, any twenty one or more, any twenty two ormore, any twenty three or more, any twenty four or more, any twenty fiveor more, any twenty-six or more, any twenty-seven or more, anytwenty-eight or more, any twenty-nine or more, any thirty or more, anythirty-one or more, any thirty-two or more, any thirty-three or more,any thirty-four or more, any thirty-five or more, any thirty-six ormore, any thirty-seven or more, any thirty-eight or more, anythirty-nine or more, any forty or more, any forty-one or more, anyforty-two or more, any forty-three or more, any forty-four or more, anyforty-five or more, any forty-six or more, any forty-seven or more, anyforty-eight or more, or all forth-nine of those listed in Table S9 ofExample 7; or

any one or more, any two or more, any three or more, any four or more,any five or more, any six or more, any seven or more, any eight or more,any nine or more, any ten or more, any eleven or more, any twelve ormore, or all thirteen of AKAP2, ARRB1, CPSF7, INO80C, JAG1, MSMP, NR4A2,PLEK, RAP1GAP2, SPEG, TRPS1, UBE2Q1, and ZNF768 is indicative ofpreeclampsia and/or an increased risk for preeclampsia in the pregnantwomen.

Embodiment 3 includes a method of Embodiment 1 or 2, wherein identifyingprotein coding sequences encoded by C-RNA molecules within the biosamplecomprises hybridization, reverse transcriptase PCR, microarray chipanalysis, or sequencing.

Embodiment 4 includes the method of Embodiment 1 or 2, whereinidentifying protein coding sequences encoded by the C-RNA moleculeswithin the biosample comprises sequencing.

Embodiment 4 includes the method of Embodiment 4, wherein sequencingcomprises massively parallel sequencing of clonally amplified molecules.

Embodiment 6 includes the method of Embodiment 4 or 5, whereinsequencing comprises RNA sequencing.

Embodiment 7 includes the method of any one of Embodiments 1 to 6,further comprising:

removing intact cells from the biosample;

treating the biosample with a deoxynuclease (DNase) to remove cell freeDNA (cfDNA);

synthesizing complementary DNA (cDNA) from C-RNA molecules in thebiosample; and/or

enriching the cDNA sequences for DNA sequences that encode proteins byexome enrichment;

prior to identifying protein coding sequence encoded by the circulatingRNA (C-RNA) molecules.

Embodiment 8 includes a method of detecting preeclampsia and/ordetermining an increased risk for preeclampsia in a pregnant female, themethod comprising:

obtaining a biological sample from the pregnant female;

removing intact cells from the biosample;

treating the biosample with a deoxynuclease (DNase) to remove cell freeDNA (cfDNA);

synthesizing complementary DNA (cDNA) from RNA molecules in thebiosample;

enriching the cDNA sequences for DNA sequences that encode proteins(exome enrichment);

sequencing the resulting enriched cDNA sequences; and

identifying protein coding sequences encoded by enriched C-RNAmolecules;

wherein protein coding sequences encoded by the C-RNA molecules encodingat least a portion of a protein selected from:

any one or more, any two or more, any three or more, any four or more,any five or more, any six or more, any seven or more, any eight or more,any nine or more, any ten or more, any eleven or more, any twelve, anythirteen or more, any fourteen or more, any fifteen or more, any sixteenor more, any seventeen or more, any eighteen or more, any nineteen ormore, any twenty or more, any twenty one or more, any twenty two ormore, any twenty three or more, any twenty four or more, any twenty fiveor more, any twenty-six or more, any twenty-seven or more, anytwenty-eight or more, any twenty-nine or more, any thirty or more, anythirty-one or more, any thirty-two or more, any thirty-three or more,any thirty-four or more, any thirty-five or more, any thirty-six ormore, any thirty-seven or more, any thirty-eight or more, anythirty-nine or more, any forty or more, any forty-one or more, anyforty-two or more, any forty-three or more, any forty-four or more, anyforty-five or more, any forty-six or more, any forty-seven or more, anyforty-eight or more, or all forth-nine of those listed in Table S9 ofExample 7; or

any one or more, any two or more, any three or more, any four or more,any five or more, any six or more, any seven or more, any eight or more,any nine or more, any ten or more, any eleven or more, any twelve ormore, or all thirteen of AKAP2, ARRB1, CPSF7, INO80C, JAG1, MSMP, NR4A2,PLEK, RAP1GAP2, SPEG, TRPS1, UBE2Q1, and ZNF768 is indicative ofpreeclampsia and/or an increased risk for preeclampsia in the pregnantwomen.

Embodiment 9 includes a method comprising:

obtaining a biological sample from the pregnant female;

removing intact cells from the biosample;

treating the biosample with a deoxynuclease (DNase) to remove cell freeDNA (cfDNA);

synthesizing complementary DNA (cDNA) from RNA molecules in thebiosample;

enriching the cDNA sequences for DNA sequences that encode proteins(exome enrichment);

sequencing the resulting enriched cDNA sequences; and

identifying protein coding sequences encoded by the enriched C-RNAmolecules;

wherein the protein coding sequences encoded by the C-RNA moleculesincludes at least a portion of a protein are selected from:

any one or more, any two or more, any three or more, any four or more,any five or more, any six or more, any seven or more, any eight or more,any nine or more, any ten or more, any eleven or more, any twelve, anythirteen or more, any fourteen or more, any fifteen or more, any sixteenor more, any seventeen or more, any eighteen or more, any nineteen ormore, any twenty or more, any twenty one or more, any twenty two ormore, any twenty three or more, any twenty four or more, any twenty fiveor more, any twenty-six or more, any twenty-seven or more, anytwenty-eight or more, any twenty-nine or more, any thirty or more, anythirty-one or more, any thirty-two or more, any thirty-three or more,any thirty-four or more, any thirty-five or more, any thirty-six ormore, any thirty-seven or more, any thirty-eight or more, anythirty-nine or more, any forty or more, any forty-one or more, anyforty-two or more, any forty-three or more, any forty-four or more, anyforty-five or more, any forty-six or more, any forty-seven or more, anyforty-eight or more, or all forth-nine of those listed in Table S9 ofExample 7; or

any one or more, any two or more, any three or more, any four or more,any five or more, any six or more, any seven or more, any eight or more,any nine or more, any ten or more, any eleven or more, any twelve ormore, or all thirteen of AKAP2, ARRB1, CPSF7, INO80C, JAG1, MSMP, NR4A2,PLEK, RAP1GAP2, SPEG, TRPS1, UBE2Q1, and ZNF768.

Embodiment 10 includes the method of any one of Embodiments 1 to 9,wherein the biosample comprises plasma.

Embodiments 11 includes the method of any one of Embodiments 1 to 10,wherein the biosample is obtained from a pregnant female at less than 16weeks gestation or at less than 20 weeks gestation.

Embodiment 12 includes the method of any one of Embodiments 1 to 10,wherein the biosample is obtained from a pregnant female at greater than20 weeks gestation.

Embodiment 13 includes a circulating RNA (C-RNA) signature for anelevated risk of preeclampsia, the C-RNA signature comprising any one ormore, any two or more, any three or more, any four or more, any five ormore, any six or more, any seven or more, any eight or more, any nine ormore, any ten or more, any eleven or more, any twelve, any thirteen ormore, any fourteen or more, any fifteen or more, any sixteen or more,any seventeen or more, any eighteen or more, any nineteen or more, anytwenty or more, any twenty one or more, any twenty two or more, anytwenty three or more, any twenty four or more, any twenty five or more,any twenty-six or more, any twenty-seven or more, any twenty-eight ormore, any twenty-nine or more, any thirty or more, any thirty-one ormore, any thirty-two or more, any thirty-three or more, any thirty-fouror more, any thirty-five or more, any thirty-six or more, anythirty-seven or more, any thirty-eight or more, any thirty-nine or more,any forty or more, any forty-one or more, any forty-two or more, anyforty-three or more, any forty-four or more, any forty-five or more, anyforty-six or more, any forty-seven or more, any forty-eight or more, orall forth-nine of those listed in Table S9 of Example 7.

Embodiment 14 includes a circulating RNA (C-RNA) signature for anelevated risk of preeclampsia, the C-RNA signature comprising any one ormore, any two or more, any three or more, any four or more, any five ormore, any six or more, any seven or more, any eight or more, any nine ormore, any ten or more, any eleven or more, any twelve or more, or allthirteen of AKAP2, ARRB1, CPSF7, INO80C, JAG1, MSMP, NR4A2, PLEK,RAP1GAP2, SPEG, TRPS1, UBE2Q1, and ZNF768.

Embodiment 15 includes a solid support array comprising a plurality ofagents capable of binding and/or identifying a C-RNA signature ofEmbodiment 13 or 14.

Embodiment 16 includes a kit comprising a plurality of probes capable ofbinding and/or identifying a C-RNA signature of Embodiment 13 or 14.

Embodiment 17 includes a kit comprising a plurality of primers forselectively amplifying a C-RNA signature of Embodiment 13 or 14.

Embodiment 18 includes the method of any one of Embodiments 1 to 12,wherein sample is a blood sample and the blood samples is collected,shipped, and/or stored in a tube that has cell- and DNA-stabilizingproperties prior to processing the blood sample into plasma.

Embodiment 19 includes the method of Embodiment 18, wherein the tubecomprises a Streck Cell-Free DNA BCT® blood collection tube.

The present invention is illustrated by the following examples. It is tobe understood that the particular examples, materials, amounts, andprocedures are to be interpreted broadly in accordance with the scopeand spirit of the invention as set forth herein.

EXAMPLES Example 1 C-RNA Signatures Unique to Pregnancy

The presence of circulating nucleic acid in maternal plasma provides awindow into the progression and health of the fetus and the placenta(FIG. 1 ). Circulating RNA (C-RNA) is detected in maternal circulationand originates from two predominant sources. A significant fraction ofC-RNA originates from apoptotic cells, which release vesicles containingC-RNA into the blood stream. C-RNA also enters maternal circulationthrough the shedding of active signaling vesicles such as exosomes andmicrovesicles from a variety of cell types. As shown in FIG. 2 , C-RNAis therefore comprised of the byproducts of cell death as well as activesignaling products. Characteristics of C-RNA include generation throughcommon processes, release from cells throughout the body, and stable andcontained in vesicles. It represents a circulating transcriptome thatreflects tissue-specific changes in gene expression, signaling, and celldeath.

C-RNA has the potential to be an excellent biomarker for at least thefollowing reasons:

1) All C-RNA is contained within membrane bound vesicles, which protectsthe C-RNA from degradation, making it quite stable in the blood.

2) C-RNA originates from all cell types. For example, C-RNA has beenshown to contain transcripts from both the placenta and the developingfetus. The diverse origins of C-RNA give it the potential to be a richrepository for accessing information on both fetal and overall maternalhealth.

C-RNA libraries were prepared from plasma samples using standardIllumina library prep and whole exome enrichment technology. This isshown in FIG. 3 . Specifically, Illumina TruSeq™ library prep and RNAAccess Enrichment were used. Using this approach, libraries weregenerated that have 90% of the reads aligning to the human coding region(FIG. 3 and FIG. 7 ). Samples were downsampled to 50 M reads and ≥40 Mmapped reads were used for downstream analysis. Samples were processedusing the C-RNA workflow shown in FIG. 3 . Dual Indexed libraries.Sequenced 50X50 on Hiseq2000

As shown in FIG. 4 , comparing results from plasma samples from thirdtrimester pregnant women to plasma samples from non-pregnant womenprovides a clear signature unique to pregnancy. The top twentydifferential abundance genes of this signature are CSHL1, CSH2, KISS1,CGA, PLAC4, PSG1, GH2, PSG3, PSG4, PSG7, PSG11, CSH1, PSG2, HSD3B1,GRHL2, LGALS14, FCGR1C, PSG5, LGALS13, and GCM1. The majority of thegenes identified in the pregnancy signature are placentally expressedand also correlate with published data. These results also confirm thatplacental RNA can be accessed in in the maternal circulation.

Example 2 C-RNA Signatures Across Gestational Age

This example characterized C-RNA signatures across different gestationalages throughout pregnancy. It is expected that the changes in C-RNAsignatures at different time points longitudinally across pregnancy willbe more subtle than the differences between C-RNA signatures of pregnantand non-pregnant samples noted in Example 1. As shown in FIG. 5 , cleartemporal changes in C-RNA profiles of the signature genes were observedas pregnancy progressed, with a clear group of genes upregulated in thefirst trimester and clear group of genes that increase in the thirdtrimester.

These genes included CGB8, CGB5, ZSCAN23, HSPA1A, PMAIP1, C8orf4, ITM2B,IFIT2, CD74, HSPA6, TFAP2A, TRPV6, EXPH5, CAPN6, ALDH3B2, RAB3B, MUC15,GSTA3, GRHL2, and CSHL1, as listed in FIG. 5 .

These genes may also include CSHL1, CSH2, KISS1, CGA, PLAC4, PSG1, GH2,PSG3, PSG4, PSG7, PSG11, CSH1, PSG2, HSD3B1, GRHL2, LGALS14, FCGR1C,PSG5, LGALS13, and GCM1.

These changes throughout the course of pregnancy correlate withpublished data from both Steve Quake and Dennis Lo. See, for example,Maron et al., 2007, “Gene expression analysis in pregnant women andtheir infants identifies unique fetal biomarkers that circulate inmaternal blood,” J Clin Invest; 117(10):3007-3019; Koh et al., 2014,“Noninvasive in vivo monitoring of tissue-specific global geneexpression in humans,” Proc Natl Acad Sci USA; 111(20):7361-6; and Ngoet al., 2018, “Noninvasive blood tests for fetal development predictgestational age and preterm delivery,” Science; 360(6393):1133-1136.C-RNA signatures correlating with patterns of gene expression of theplacenta were found. Thus, this approach is able to detect subtlechanges within pregnancy and provides non-invasive means to monitorplacental health.

Example 3 C-RNA Signatures of Preeclampsia

With this example, C-RNA signatures unique to preeclampsia wereidentified. C-RNA signatures were determined in samples collected frompregnant women diagnosed with preeclampsia from two studies, the RGH14Study (registered with clinical trials.gov as NCT0208494) and the PearlStudy (also referred to herein as the Pearl Biobank; registered withclinical trials.gov as NCT02379832)), were assayed (FIG. 6 ). Two tubesof blood were collected at the time of diagnosis for preeclampsia.Eighty controls samples matched for gestational age were collected tominimize transcriptional variability not related to the preeclampsiadisease state and to control for gestational age differences in C-RNAsignatures. Samples from the RGH14 study were used to identify a set ofbiologically relevant genes, and the predictive value of thesebiomarkers was validated in an independent cohort of samples from thePearl Biobank.

In the analysis of the RGH14 data, C-RNA signatures unique topreeclampsia (PE) were identified using four different methods, theTREAT method, a Bootstrap method, a jackknifing method, and the Adaboostmethod. Example 3 focuses on the first 3 analysis methods and Examplefour focuses on the Adaboost method.

The t-test relative to threshold (TREAT) statistical method utilizingthe EDGR program allows researchers to formally test (with associatedp-values) whether the differential expression in a microarray experimentis greater than a given (biologically meaningful) threshold. SeeMcCarthy and Smyth, 2009 “Testing significance relative to a fold-changethreshold is a TREAT,” Bioinformatics; 25(6):765-71 for a more detaileddescription of the TREAT statistical method and Robinson et al., 2010,“edgeR: a Bioconductor package for differential expression analysis ofdigital gene expression data,” Bioinformatics; 26:139-140 for a moredetailed description of the EDGR program. See Freund and Schapire, 1997,“A Decision-Theoretic Generalization of On-Line Learning and anApplication to Boosting,” Journal of Computer and Systems Sciences;55(1):119-139 and Pedregosa et al., 2011, “Scikit-learn: MachineLearning in Python,” JMLR; 12:2825-2830 for a more detailed descriptionof the Adaboost method. The Adaboost method will be discussed in Example4.

In the first method, standard statistical testing (TREAT method) wasused to identify genes that are statistically different in the RGH14preeclampsia cohort of 40 patients as compared to a subset of matchedcontrols (40 patients). 122 genes were identified as statisticallydifferent in the preeclampsia cohort (40 patients) as compared to asubset of matched controls (40 patients) (FIG. 8 , right panel). Thesegenes include CYP26B1, IRF6, MYH14, PODXL, PPP1R3C, SH3RF2, TMC7,ZNF366, ADCY1, C6, FAM219A, HAO2, IGIP, IL1R2, NTRK2, SH3PXD2A, SSUH2,SULT2A1, FMO3, FSTL3, GATA5, HTRA1, C8B, H19, MN1, NFE2L1, PRDM16,AP3B2, EMP1, FLNC, STAG3, CPB2, TENC1, RP1L1, A1CF, NPR1, TEK, ERRFI1,ARHGEF15, CD34, RSPO3, ALPK3, SAMD4A, ZCCHC24, LEAP2, MYL2, NRG3,ZBTB16, SERPINA3, AQP7, SRPX, UACA, ANO1, FKBP5, SCN5A, PTPN21, CACNA1C,ERG, SOX17, WWTR1, AIF1L, CA3, HRG, TAT, AQP7P1, ADRA2C, SYNPO, FN1,GPR116, KRT17, AZGP1, BCL6B, KIF1C, CLIC5, GPR4, GJA5, OLAH, C14orf37,ZEB1, JAG2, KIF26A, APOLD1, PNMT, MYOM3, PITPNM3, TIMP4, HTRA4, AMPH,LCN6, CRH, TEAD4, ARMS2, PAPPA2, SEMA3G, ADAMTS1, ALOX15B, SLC9A3R2,TIMP3, IGFBP5, HSPA12B, PRG2, PRX, ARHGEF25, ADAMTS2, DAAM2, FAM107A,LEP, NES, VSIG4, HBG2, CADM2, LAMPS, PTGDR2, NOMO1, NXF3, PLD4, BPIFB3,PACSIN1, CUX2, FLG, CLEC4C, and KRT5.

The TREAT method did not identify a set of genes that 100% accuratelyclassifies the preeclampsia patients into a separate group (FIG. 15 ).However, focusing in on these identified genes did improveclassification compared to using the entire data set of all measuredgenes (FIG. 8 , left panel). This highlights the value of focusing in ona subset of genes for prediction. However, with the TREAT method, asignificant amount of variability was observed in the genes identifieddepending on which controls were selected. To deal with this biologicalvariability and further improve the predictive value of our gene list, asecond bootstrapping approach was developed.

In the RGH14 study more control samples (80) are available thanpreeclampsia patient samples (40). Thus, the RGH14 cohort of 40preeclampsia patient samples was compared to a random selection of 40controls samples (still matched for gestational age) and a gene listthat is statistically different in the preeclampsia cohort wasidentified. As shown in FIG. 9 , this was then repeated 1,000 times, toidentify how often a set of genes was identified. A significant subsetof genes only show up less than 10 times out of the 1,000 iterations(less than 1% of the 1,000 iterations). These low frequency genes mostlikely are due to biological noise and may not reflect a gene that isuniversally specific to preeclampsia. So, the gene list was furtherdownselected by requiring a gene to be considered as statisticallydifferent in the preeclampsia cohort only if identified in 50% of the1,000 iterations performed (FIG. 9 , right panel). As shown in FIG. 10 ,differential transcript abundance with the additional bootstrappingselection distinguishes preeclampsia samples from healthy controls.Using this additional requirement helped address biological variabilityand further improved the ability to classify preeclampsia samplescorrectly.

Using this Bootstrap method, 27 genes were identified as statisticallyassociated with preeclampsia. These genes include TIMP4, FLG, HTRA4,AMPH, LCN6, CRH, TEAD4, ARMS2, PAPPA2, SEMA3G, ADAMTS1, ALOX15B,SLC9A3R2, TIMP3, IGFBP5, HSPA12B, CLEC4C, KRT5, PRG2, PRX, ARHGEF25,ADAMTS2, DAAM2, FAM107A, LEP, NES, and VSIG4. The genes identified withthis bootstrapping method had excellent concordance with published data.Approximately 75% of these genes are expressed by the placenta. As shownin FIG. 11 , there is overlap with known markers of preeclampsia,including PAPPA and CRH. And, a significant number of these genes areinvolved in embryo development, extracellular matrix remodeling, immuneregulation, and cardiovascular function, all pathways known to bedysregulated in preeclampsia.

A third jackknifing approach was also developed to capture the subset ofgenes with the highest predictive value. This approach is similar to thebootstrapping method. Patients from both preeclampsia and control groupswere randomly subsampled and differentially abundant genes identified1,000 times. Instead of using the frequency with which a gene isidentified as statistically different, the jackknifing approachcalculated confidence intervals (95%, one-sided) for the p-value of eachtranscript. Genes where this confidence interval exceeded 0.05 wereexcluded. (FIG. 16 , left panel).

Using the jackknifing approach, 30 genes were identified as predictiveof preeclampsia: VSIG4, ADAMTS2, NES, FAM107A, LEP, DAAM2, ARHGEF25,TIMP3, PRX, ALOX15B, HSPA12B, IGFBP5, CLEC4C, SLC9A3R2, ADAMTS1, SEMA3G,KRT5, AMPH, PRG2, PAPPA2, TEAD4, CRH, PITPNM3, TIMP4, PNMT, ZEB1,APOLD1, PLD4, CUX2, HTRA4.

As shown in FIG. 16 right panel, this approach gave good classificationof preeclampsia patients in the RGH14 data set (compare FIG. 15 (TREAT),FIG. 10 (bootstrapping) and FIG. 16 (jackknifing)). Each identified genelist was also used to classify preeclampsia samples in the independentPearl Biobank dataset. As shown in FIG. 17 , each gene list was able toclassify preeclampsia samples.

All genes identified by the bootstrapping and jackknifing methods arerepresented in the 122 TREAT method genes (Table 2, DEX analysis, TruSeqlibrary prep method). The bootstrapping and jackknifing approach genelists are highly concordant, with over 70% of genes in common. Nearly90% of transcripts identified by any approach exhibit increasedtranscript abundance in preeclampsia patients, consistent with elevatedsignaling and/or cell death in this disease.

Example 4 Identification of C-RNA Signatures with Adaboost

With this example an alternative approach, a publicly available machinelearning algorithm called adaboost, was used to identify a specificC-RNA signature associated with preeclampsia. As shown in FIG. 12 , thisapproach identifies a set of genes that has the most predictive power toclassify a sample as preeclampsia (PE) or normal. Using this gene list,the clearest separation of a preeclampsia cohort from healthy controlswas observed. However, this approach can also be very susceptible toovertraining to the samples used to build the model. Thus, thepredictive model was validated using a completely independent data setfrom the PEARL study (FIG. 13 ). Using this Adaboost gene list, 85% ofthe preeclampsia samples were accurately classify with 85% specificity(FIG. 14 ). Overall, the Adaboost machine learning approach built themost accurate predictive model for preeclampsia.

Using the Adaboost method, 75 genes were identified as statisticallyassociated with preeclampsia (Table 3, AdaBoost Analysis, TruSeq libraryprep method). These genes include ARRDC2, JUN, SKIL, ATP13A3, PDE8B,GSTA3, PAPPA2, TIPARP, LEP, RGP1, USP54, CLEC4C, MRPS35, ARHGEF25, CUX2,HEATR9, FSTL3, DDI2, ZMYM6, ST6GALNAC3, GBP2, NES, ETV3, ADAM17, ATOH8,SLC4A3, TRAF3IP1, TTC21A, HEG1, ASTE1, TMEM108, ENC1, SCAMP1, ARRDC3,SLC26A2, SLIT3, CLIC5, TNFRSF21, PPP1R17, TPST1, GATSL2, SPDYE5, HIPK2,MTRNR2L6, CLCN1, GINS4, CRH, C10orf2, TRUB1, PRG2, ACY3, FAR2, CD63,CKAP4, TPCN1, RNF6, THTPA, FOS, PARN, ORAI3, ELMO3, SMPD3, SERPINF1,TMEM11, PSMD11, EBI3, CLEC4M, CCDC151, CPAMD8, CNFN, LILRA4, ADA,C22orf39, PI4KAP1, and ARFGAP3.

A refined AdaBoost model was also developed for robust classification ofPE samples. In order to create a generalized machine learning model thatcould accurately predict new samples, we used a rigorous approach thatavoided overfitting to a single dataset and validated the finalclassifier with samples not used for model building. As illustrated inFIG. 18 , the RGH14 dataset was divided into 6 pieces by randomselection: a holdout subset with 12% of samples which was excluded frommodel building, and 5 evenly sized test subsets. For each iterationsubsets were designated as training data or test samples. This process,starting at building the AdaBoost model was repeated for a minimum of 10times on this data subset. After 50 high performing models were builtfor the 5 test-train subsets, the estimators from all models were mergedinto a single AdaBoost model.

Using the refined AdaBoost model, 11 genes were identified as staticallyassociated with preeclampsia. These genes include CLEC4C, ARHGEF25,ADAMTS2, LEP, ARRDC2, SKIL, PAPPA2, VSIG4, ARRDC4, CRH and NES. Theperformance of this predictive model was validated using the hold outdata set from RGH14 as well as in the completely independent PearlBiobank cohort (FIG. 19 ).

AdaBoost Model Creation Description. The AdaBoost classificationapproach was refined in order to obtain more specific gene sets(AdaBoost Refined 1-7) by the following approach, also illustrated inFIG. 18 . The RGH14 dataset was divided into 6 pieces by randomselection: a holdout subset with 12% of samples which was excluded frommodel building, and 5 evenly sized test subsets.

For each of the test subsets, training data was assigned as all samplesin neither the holdout or test samples. Gene counts for the test andtraining samples were TMM-normalized in edgeR, then standardized suchthat the training data has mean of 0 and standard deviation of 1 foreach gene. An AdaBoost model with 90 estimators and 1.6 learning ratewas then fit to the training data. Feature pruning was then performed bydetermining the feature importance of each gene in the model and testingthe impact of eliminating estimators using genes with importance below athreshold value. The threshold resulting in the best performance (asmeasured by Matthew's correlation coefficient on test dataclassification) with the fewest genes was selected, and that modelretained. This process, starting at building the AdaBoost model wasrepeated for a minimum of 10 times on this data subset.

After all 50 plus models were built for the 5 test-train subsets, theestimators from all models were merged into a single AdaBoost model.Feature pruning was performed again, this time using the percent ofmodels incorporating a gene to for threshold values and assessingperformance with the average negative log loss value for theclassification of each test subset. The model which obtained the maximalnegative log loss value with the fewest genes was selected as the finalAdaBoost model.

AdaBoost Gene Lists. Upon repetition of this process, slight variationswere observed in the genes selected for the final model, due to innaterandomization in the AdaBoost algorithm implementation, howeverperformance remained high for predicting the test data, holdout data,and independent (Pearl) datasets.

Eleven total genes were observed in at least one of 14 AdaBoost Refinedmodels generated: ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, CRH, LEP,NES, PAPPA2, SKIL, VSIG4 (AdaBoost Refined 1), although no models weregenerated that included all simultaneously.

Two observed gene sets offered the highest performance on classificationof independent data. These are AdaBoost Refined 2: ADAMTS2, ARHGEF25,ARRDC2, CLEC4C, LEP, PAPPA2, VSIG4 and AdaBoost Refined 3: ADAMTS2,ARHGEF25, ARRDC2, CLEC4C, LEP, PAPPA2, SKIL, VSIG4.

Four additional gene sets performed almost as highly as AdaBoost Refined2-3. These are AdaBoost Refined 4: ADAMTS2, ARHGEF25, ARRDC4, CLEC4C,LEP, NES, SKIL, VSIG4; AdaBoost Refined 5: ADAMTS2, ARHGEF25, ARRDC2,ARRDC4, CLEC4C, CRH, LEP, PAPPA2, SKIL, VSIG4; AdaBoost Refined 6:ADAMTS2, ARHGEF25, ARRDC2, CLEC4C, LEP, SKIL; and AdaBoost Refined 7:ADAMTS2, ARHGEF25, ARRDC2, ARRDC4, CLEC4C, LEP, PAPPA2, SKIL.

Example 5 Identification of C-RNA Signature with Transposome BasedLibrary Prep

The RGH14 samples were also processed through the Illumina Nextera Flexfor Enrichment protocol, enriched for whole exome and sequenced to >40million reads. This approach is more sensitive and robust for lowinputs, thus likely to identify additional genes predictive ofpreeclampsia. This dataset was run through three analysis methods,standard differential expression analysis (TREAT), jackknifing, and therefined Adaboost model. See Example 3 and Example 4 for detaileddescription of these analysis methods.

Changing the method for generating libraries altered the genes detectedin all three analysis methods. For the TREAT method, 26 genes wereidentified as differentially abundant in preeclampsia, with the majorityagain showing elevated abundance in preeclampsia (See Table 2, DEXAnalysis, Nextera Flex for Enrichment library prep method). These genesinclude ADAMTS1, ADAMTS2, ALOX15B, AMPH, ARHGEF25, CELF4, DAAM2,FAM107A, HSPA12B, HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES,OLAH, PACSIN1, PAPPA2, PRX, PTGDR2, SEMA3G, SLC9A3R2, TIMP3, VSIG4. FIG.20 shows classification of the RGH14 samples with this gene list.

Applying the jackknifing analysis method downselected the TREAT list to22 genes identified as differentially abundant in preeclampsia. Thesegenes included ADAMTS1, ADAMTS2, ALOX15B, ARHGEF25, CELF4, DAAM2,FAM107A, HTRA4, IGFBP5, KCNA5, KRT5, LCN6, LEP, LRRC26, NES, OLAH, PRX,PTGDR2, SEMA3G, SLC9A3R2, TIMP3, VSIG4. The improved performance of thislist is shown in FIG. 20 .

The refined AdaBoost model approach was applied to this data, asdescribed in Example 4. Using this method, 24 genes are identified asstatically associated with preeclampsia (Table 3, AdaBoost Analysis,Nextera Flex for Enrichment library prep method). These genes includeLEP, PAPPA2, KCNA5, ADAMTS2, MYOM3, ATP13A3, ARHGEF25, ADA, HTRA4, NES,CRH, ACY3, PLD4, SCT, NOX4, PACSIN1, SERPINF1, SKIL, SEMAG3, TIPARP,LRRC26, PHEX, LILRA4, and PER1. The performance of this predictive modelis indicated in FIG. 21 .

Example 6 Circulating Transcriptome Measurements from Maternal BloodDetects Early-Onset Preeclampsia Signature

Molecular tools to non-invasively monitor pregnancy health fromconception to birth would enable accurate detection of pregnancies atrisk for adverse outcomes. Circulating RNA (C-RNA) is released by alltissues into the bloodstream, offering an accessible, comprehensivemeasurement of placental, fetal and maternal health (Koh et al., 2014,Proceedings of the National Academy of Sciences; 111:7361-7366; and Tsuiet al., 2014, Clinical Chemistry; 60:954-962). Preeclampsia (PE), aprevalent and potentially fatal pregnancy complication, is placental inorigin but gains a substantial maternal component as the diseaseprogresses (Staff et al., 2013, Hypertension; 61:932-942; andChaiworapongsa et al., 2014, Nature Reviews Nephrology; 10, 466-480).Yet purported biomarkers have shown limited clinical utility (Poon andNicolaides, 2014, Obstetrics and Gynecology International; 2014:1-11;Zeisler et al., 2016, N Engl J Med; 374:13-22; and Duhig et al., 2018,F1000Research; 7:242). Hypothesizing that characterization of thecirculating transcriptome may identify better biomarkers, C-RNA wasanalyzed from 113 pregnancies, 40 at the time of early-onset PEdiagnosis. Using a novel workflow, differences were identified in theabundance of 30 transcripts which are consistent with the biology of PEand represent placental, fetal, and maternal contributions. Further, amachine learning model was developed, demonstrating that only sevenC-RNA transcripts are required to classify PE in two independentscohorts (92-98% accuracy). The global measurements of C-RNA disclosed inthis example highlight the utility in monitoring both maternal and fetalhealth and hold great promise for the diagnosis and prediction ofat-risk pregnancies.

Several studies have begun to investigate and identify potentialbiomarkers in C-RNA for a range of pregnancy complications (Pan et al.,2017, Clinical Chemistry; 63:1695-1704; Whitehead et al., 2016, PrenatalDiagnosis; 36:997-1008; Tsang et al., 2017, Proc Natl Acad Sci USA; 114:E7786-E7795; and Ngo et al., 2018, Science; 360:1133-1136). However,these studies have involved few patients and have been limited tomonitoring small numbers of genes—almost exclusively placental and fetalderived transcripts. Measurements of the entire circulatingtranscriptome are difficult to perform because they require specificupfront sample collection and processing to minimize variability andcontamination from cell lysis (Chiu et al., 2001, Clinical Chemistry;47:1607-1613; and Page et al., 2013, PLoS ONE; 8: e77963). This complexworkflow makes large clinical sample collections difficult to achievebecause the labor required for immediate processing of blood samples isinfeasible for many clinics (Marton and Weiner, 2013, BioMed ResearchInternational; 2013:891391). Therefore, with this example, a method wasestablished that allows overnight shipment of blood to a processing labwhere every step of sample preparation is performed in a controlledenvironment, providing a scalable platform for clinical trial levelassessments (FIG. 22A).

The lynchpin of this method is the ability to ship blood overnight to aprocessing lab. The C-RNA pregnancy signal was assessed after overnight,room-temperature shipping in several tube types (FIGS. 26A-26C). Bloodstored in EDTA tubes, the gold standard used by prior C-RNA studies,exhibited a reduction in the abundance of pregnancy-associatedtranscripts and overall instability of the transcriptomic profile (Qinet al., 2013, BMC Research Notes; 6:380). In contrast, the predominanttube type used for Non-Invasive Prenatal Testing (NIPT), Cell-Free DNABCT (Streck), retained the signal from placental transcripts and hadimproved technical reproducibility (FIG. 26B) (Medina Diaz et al., 2016,PLoS ONE; 11:e0166354).

Shipment of blood allowed us to easily obtain an average of 5 mL plasmaper patient from a single tube of blood. The difference in C-RNA dataquality was assessed when using varying plasma volumes and determinedthat using <2 mL plasma significantly increased noise and decreasedlibrary complexity (FIGS. 27A and 27B). Thus 4 mL of plasma was used forthe studies of this example to maximize confidence in data quality.

This novel workflow was validated by recapitulating previous workmonitoring C-RNA dynamics of >10,000 transcripts per healthy pregnancyfrom first to third trimester. Using 152 samples collected serially from45 healthy pregnancies (Pre-Eclampsia and Growth RestrictionLongitudinal Study Control Cohort—PEARL; NCT02379832; Table 5), 156significantly altered transcripts were identified, with the majorityincreasing in abundance as pregnancy progresses (FIG. 22B). 42% of thealtered genes were identified in prior C-RNA studies (FIG. 22C) (Koh etal., 2014, Proceedings of the National Academy of Sciences;111:7361-7366; and Tsui et al., 2014, Clinical Chemistry; 60:954-962).Of the 91 transcripts identified only in this study, 64% are expressedby placental and/or fetal tissues (FIGS. 22D and 28A-28C). Presumably,the remaining genes reflect the maternal response to pregnancy.

Study Design

For the next phase of investigation, the workflow was applied onclinical samples to measure C-RNA changes in PE (iPC, IlluminaPreeclampsia Cohort). PE is a heterogeneous disorder and associated withdifferent severity and patient outcomes based on whether it manifestsbefore (early-onset) or after (late-onset) 34 gestational weeks (Staffet al., 2013, Hypertension; 61:932-942; Chaiworapongsa et al., 2014,Nature Reviews Nephrology; 10, 466-4803; and Dadelszen et al., 2003,Hypertension in Pregnancy; 22:143-148). This study to focused on themore severe early-onset form of PE and defined strict diagnosticcriteria with clear inclusion and exclusion requirements—most criticallyexcluding any individuals with a history of chronic hypertension—inorder to obtain a clean cohort (Table 6) (Nakanishi et al., 2017,Pregnancy Hypertension; 7:39-43; and Hiltunen et al., 2017, PLoS ONE;12:e0187729). Maternal characteristics, pregnancy outcomes, andmedications in use were recorded throughout the study (Table 7). 113samples were collected across 8 sites (Table 8), 40 at the time of PEdiagnosis, and 73 controls gestationally-age matched within 1 week (FIG.23A). All but one woman with PE gave birth prematurely, in contrast to9.5% of controls, confirming these diagnostic criteria as identifyingindividuals severely impacted by this disease (FIG. 23C).

All samples were randomly distributed across multiple processingbatches, then sequenced to ≥40 M reads. Standard differential expressionanalysis using the full cohort identified 42 altered transcripts, with37 increased in PE (FIG. 24A, blue and orange). However, of concern wasthe high variability observed in the genes detected as altered whendifferent subsets of controls were selected for analysis.

To address this discrepancy, a jackknifing approach was incorporatedwhich allowed the identification of the genes that are most consistentlyaltered (FIGS. 24A and 24B, orange). 1,000 iterations of differentialanalysis with randomly selected sample subsets were performed, whichallowed the construction of confidence intervals for the p-valuesassociated with each putatively altered transcript (FIG. 29A). 12 geneswhose confidence interval exceeded 0.05 were excluded (FIG. 24B). Thesegenes would not have been excluded by simply setting a threshold forbaseline abundance or biological variance (FIG. 29B), however it wasobserved that these transcripts have lower predictive value (FIG. 29C).Hierarchical clustering indicates these genes are not altereduniversally in the PE cohort, and thus lack sensitivity (73%) foraccurate classification of this condition (FIG. 29D).

The analysis then focused on the refined 30 gene set, 60% of which havepreviously been associated with PE (Namli et al., 2018, Hypertension inPregnancy; 37: 9-17; Than et al., 2018, Frontiers in Immunology; 9:1661;Kramer et al., 2016, Placenta; 37:19-25; Winn et al., 2008,Endocrinology; 150:452-462; and Liu et al., 2018, Molecular MedicineReports; 18:2937-2944). qPCR analysis confirmed 19 of 20 genes assignificantly altered in PE (FIG. 24C, Table 9). Strikingly, 40% ofthese genes encode for extracellular or secreted protein products.Additionally, nearly all genes are involved in PE relevant processes,including extracellular matrix (ECM) remodeling, pregnancy duration,placental/fetal development, angiogenesis, and hypoxia response (Table10). 67% of these transcripts were expressed by the placenta and/orfetus (FIG. 24D). In the remaining maternally expressed transcripts,cardiovascular and immune functions were well represented (Table 10).Hierarchical clustering of these genes effectively segregated PE andcontrol samples with 98% sensitivity and 97% specificity (FIG. 24E).Intriguingly, clinical data for the two misidentified controls indicatedpotentially confounding health problems, as suggested by their use ofhypertensive medication (Table 7).

Using the genes identified in iPC, the ability to cluster a cohort ofsamples obtained from an independent biobank was assessed—thePre-Eclampsia and Growth Restriction Longitudinal Study (PEARL;NCT02379832; FIGS. 23B and 23C, Table 11). This cohort consisted of bothearly-(diagnosed at <34 weeks); and late-onset PE with gestationallyage-matched controls. Early-onset PE samples clustered separately frommatched controls with 83% sensitivity and 92% specificity, furthervalidating the relevance of these transcripts (FIG. 24F). In contrast,no clustering was observed for the late-onset PE and matched controlsamples (FIG. 24G).

The iPC data was then used to build an AdaBoost model for robustclassification of PE samples. In order to create a generalized machinelearning model that could accurately predict new samples, a rigorousapproach was used that avoided overfitting to a single dataset andvalidated the final classifier with samples not used for model building(FIGS. 30A-30D and FIGS. 31A-31E). Surprisingly, the final model onlyutilized 7 genes, 3 of which have not been previously reported (FIG.25A). For the entire iPC cohort, this model classified samples withextremely high accuracy (AUC=0.99, sensitivity=98%, specificity=99%;FIGS. 25B and 25C, blue). Early-onset PE PEARL samples were alsoaccurately classified (AUC=0.88, sensitivity=100%, specificity=83%;FIGS. 25B and 25C, pink). Unexpectedly, late-onset PE PEARL samples werealso classified with reasonable accuracy (AUC=0.74, sensitivity=75%,specificity=67%; FIGS. 25B and 25C, green).

This gene set was highly concordant with transcripts identified bydifferential abundance analysis (FIG. 25D; Table 10). The classifierrelied on both placentally and maternally expressed transcripts (FIG.25E). All genes used by the model form protein products that are eitherextracellular or membrane bound. Despite the small number of genesselected by AdaBoost, a diversity of PE-relevant functions was observed,specifically cardiovascular function and angiogenesis, immuneregulation, fetal development, and ECM remodeling.

Methods

Prospective Clinical Sample Collection. Pregnant patients were recruitedin an Illumina sponsored clinical study protocol in compliance with theInternational Conference on Harmonization for Good Clinical Practice.Following informed consent, 20 mL whole blood samples were collectedfrom 40 pregnant women with a diagnosis of preeclampsia before 34 weeksgestation with severe features defined per ACOG guidelines (Table 6).Samples from 76 healthy pregnancies were also collected and were matchedfor gestational age to the preeclampsia group. Three control samplesdeveloped term preeclampsia after blood collection and were excludedfrom data analysis. For detailed inclusion and exclusion criteria, seeTable 6. Patient clinical history, treatment and birth outcomeinformation were also recorded (Table 7).

Patients were recruited across 8 different clinical sites, includingUniversity of Texas Medical Branch (Galveston, Tex.), Tufts MedicalCenter (Boston, Mass.), Columbia University Irving Medical Center (NewYork, N.Y.), Winthrop University Hospital (Mineola, N.Y.), St. Peter'sUniversity Hospital (New Brunswick, N.J.), Christiana Care (Newark,Del.), Rutgers University Robert Wood Johnson Medical School (NewBrunswick, N.J.) and New York Presbyterian/Queens (New York, N.Y.). Theclinical protocol and informed consent were approved by each clinicalsite's Institutional Review Board. See Table 8 for patient distributionacross clinical sites.

PEARL Validation Cohort Study Design. Illumina obtained plasma samplesfrom the Preeclampsia and Growth Restriction Longitudinal study (PEARL;NCT02379832) to be used as an independent validation cohort. Plasmasamples were obtained after the study had reached completion. PEARLsamples were collected at the Centre hospitalier universitaire de Québec(CHU de Québec) with principal investigator Emmanual Buj old, MD, MSc. Agroup of 45 control pregnancies and 45 case pregnancies were recruitedin this study and written informed consent was obtained for allpatients. Only participants above 18 years of age were eligible, and allpregnancies were singleton.

Preeclampsia Group. The criteria for preeclampsia was defined based onthe Society of Obstetricians and Gynecologists of Canada (SOGC) June2014 criteria for preeclampsia, with a gestational age requirementbetween 20 and 41 weeks. A blood sample was taken once at the time ofdiagnosis.

Control Group. 45 pregnant women who were expected to have a normalpregnancy were recruited between 11 and 13 weeks gestational age. Eachenrolled patient was followed longitudinally with blood drawn at 4timepoints throughout pregnancy until birth. The control women weredivided into three subgroups and subsequent follow up blood draws werestaggered to cover the entire range of gestational ages throughoutpregnancy (Table 5).

The PEARL control samples were used for two purposes. 153 longitudinalsamples from 45 individual women were used to monitor placental dynamicsthroughout pregnancy. Additionally, control samples were selected forcomparison to the preeclampsia cohort, which were matched forgestational age and used to validate the model.

Study Sample Processing. All samples from the Illumina prospectivecollection and the PEARL samples were processed identically byinvestigators blinded to disease status. Two tubes of blood werecollected per patient in Cell-Free DNA BCT tubes (Streck) following themanufacturer instructions. Blood samples were stored and shipped at roomtemperature overnight and processed within 72 hours. Blood wascentrifuged at 1,600×g for 20 minutes at room temperature, plasmatransferred to a new tube and centrifuged additional 10 minutes at16,000×g to remove residual cells. Plasma was stored at −80° C. untiluse. Circulating RNA was extracted from 4.5 mL of plasma using theCirculating Nucleic Acid Kit (Qiagen) followed by DNAse I digestion(Thermofisher) according to manufacturer's instructions.

cDNA Synthesis and Library Prep. Circulating RNA was fragmented at 94°C. for 8 minutes followed by random hexamer primed cDNA synthesis usingthe Illumina TruSight Tumor 170 Library Prep kit (Illumina). Illuminasequencing library prep was carried out according to TST170 TumorLibrary Prep Kit for RNA, with the following modifications toaccommodate low RNA inputs. All reactions were reduced to 25% oforiginal volume and the ligation adaptor was used at 1 in 10 dilution.Library quality was assessed using High Sensitivity DNA Analysis kits onthe Agilent Bioanalzyer 2100 (Agilent).

Whole Exome Enrichment. Sequencing libraries were quantified usingQuant-iT PicoGreen dsDNA Kit (ThermoFisher Scientific), normalized to200 ng input and pooled to 4 samples per enrichment reaction. Wholeexome enrichment was carried out according to the TruSeq RNA AccessLibrary Prep guide (Illumina). Additional blocking oligos lacking the 5′biotin designed against hemoglobin genes HBA1, HBA2, and HBB wereincluded in the enrichment reaction to reduce enrichment of these genesin the sequencing libraries. Final enrichment libraries were quantifiedusing Quant-IT Picogreen dsDNA Kit (ThermoFisher Scientific), normalizedand pooled for paired end 50 by 50 sequencing on Illumina HiSeq 2000platforms to a minimum depth of 40 million reads per sample.

Data Analysis. Unless otherwise noted, all statistical testing wastwo-sided. Non-parametric testing was used when data were not normallydistributed. Sequencing reads were mapped to human reference genome(hg19) with tophat (v2.0.13), and transcript abundance quantified withfeatureCounts (subread-1.4.6) against RefGene coordinates (obtained Oct.27, 2014). Tissue expression data were obtained from Body Atlas(CorrelationEngine, BaseSpace, Illumina, Inc) (Kupershmidt, et al.,2010, PLoS ONE 5; 10.1371/journal.pone.0013066). vGenes with expression≥2-fold higher than the median expression across all tissues in theplacenta or any of the fetal tissues (brain, liver, lung, and thyroid)were assigned to that group. Subcellular localization was obtained fromUniProt.

Differential expression analysis was performed in R (v3.4.2) with edgeR(v3.20.9), after exclusion of genes with a CPM ≤0.5 in <25% of samples.The dataset was normalized by the TMM method, and differentiallyabundant genes identified by the glmTreat test for a log fold change ≥1followed by Bonferroni-Holm p-value correction. The same process wasused for each jackknifing iteration, using 90% of samples in each groupselected by random sampling without replacement. After 1,000 jackknifingiterations, the one-sided 95% confidence interval for gene-wise p-valueswas calculated with statsmodels (v0.8.0). Hierarchical clusteringanalysis was performed with squared Euclidean distance and averagelinkage.

AdaBoost was performed in python with scikit-learn (v0.19.1,sklearn.ensemble. AdaBoostClassifier). Optimal hyperparameter values (90estimators, 1.6 learning rate) were determined by grid search, usingMatthew's correlation coefficient to quantify performance. The overallAdaBoost model development strategy is illustrated in FIGS. 31A-31E.Datasets (TMM-normalized log CPM values of genes with a CPM ≤0.5 in <25%of samples) were standardized (sklearn.preprocessing. StandardScaler)prior to fitting classifiers. The same scaler fit on training data wasapplied to the corresponding testing dataset; all 5 scalers for the 5training datasets were averaged for use with the final model. Thedecision function score was used to construct ROC curves and determinesample classification.

RT-qPCR validation assay and analysis. C-RNA was isolated and convertedto cDNA from 2mls of plasma from 19 Preeclampsia (PE) and 19 matchedcontrol samples, which were selected randomly. The cDNA waspre-amplified using the TaqMan Preamp master Mix (cat: 4488593) for 16cycles and diluted 10-fold to a final volume of 500 μL. For qPCR, thereaction mixture contained 54, of diluted pre-amplified cDNA, 104, ofTaqMan gene expression master mix (cat: 4369542), 1 μL of TaqMan Probe,and 44, of water using the manufacturer's instructions. For each TaqManprobe (Table 9), three qPCR reactions were carried out per diluted cDNAsample and the Cq values were determined using Bio-Rad CFX managersoftware. To determine gene abundance for each target gene, theΔΔCq=2{circumflex over ( )}−(target Cq−ref Cq_(avg)) was calculatedusing the mean Cq values between five reference gene probes (refCq_(avg)). To determine the fold change (PE/CTRL) for each probe, theΔΔCq values for each sample was divided by the average ΔΔCq value forthe matched control group.

Tube type study. To assess the effects of tube type and overnightshipping on circulating RNA quality, blood was drawn from pregnant andnon-pregnant females in the following tube types: K2 EDTA (BecktonDickinson), ACD (Beckton Dickinson), Cell Free RNA BCT tube (Streck),and 1 Cell Free DNA BCT tube (Streck). 8 mL of blood was drawn into eachtube and shipped overnight either on ice packs (EDTA and ACD) or shippedat room temperature (Cell Free RNA and DNA BCT tubes). All shipped bloodtubes were processed into plasma within 24 hours of the blood draw. As areference, 8 mL of blood was also drawn into K2 EDTA tubes and processedwithin 4 hours into plasma on site and shipped as plasma on dry ice. Allplasma processing and circulating RNA extraction was carried out asdescribed in the methods section. 3 mL of plasma was used per conditionto generate sequencing libraries for enrichment using Illumina protocolsas described.

Reproducibility Study. Plasma was obtained from 10 individuals and splitinto 4 mL, 1 mL, and 0.5 mL volumes, with 3 replicates for each volume.Circulating RNA extraction (Qiagen Circulating Nucleic Acid Kit) andrandom primed cDNA synthesis were carried out on all samples aspreviously described. For libraries using 4.5 mL plasma inputs,sequencing libraries were generated using the TST170 Tumor Library PrepKit as described above. For 1 mL and 0.5 mL inputs, the Accel-NGS 1SPlus DNA Library Kit (Swift Biosciences) was used to generate libraries.Whole exome enrichment and sequencing was carried out on all samplesusing as described above.

Discussion

This study focused on identifying differences that are universal toearly-onset PE, supporting the ultimate goal of clinically actionablebiomarker discovery. This required tailoring the analysis methods toaccount for the variability observed in the data. This variance stemsfrom both the substantial biological noise in C-RNA measurements as wellas the phenotypic diversity of PE. C-RNA is inherently more variablethan single tissue transcriptomics because it represents a combinationof cell death, signaling, and gene expression across all organs.Furthermore, PE exhibits a wide range of maternal and fetal outcomeswhich may be associated with different underlying molecular causes.While the genes that were eliminated may be biologically relevant in PE,they were not universal in the cohort. Interestingly, the excludedtranscripts were elevated in specific women, who may represent amolecular subset of PE.

Larger cohorts will help elucidate if C-RNA can delineate PE subtypes,which is crucial to understanding the diverse pathophysiology of thiscondition.

The most universal set of transcripts was identified by AdaBoost. Thesuccess of this method was underscored by highly accurate classificationof an independent early-onset PE cohort (PEARL). These samples werecollected from a different population with significantly relaxedinclusion and exclusion criteria, for instance including women in thecontrol group who had chronic hypertension, gestational diabetes, orAlport syndrome—none of which were misidentified as having PE. Incontrast to hierarchical clustering, 17 of 24 individuals from thelate-onset PE cohort were correctly classified by machine learning modelof this example, surprising given the suggestion that early- andlate-onset PE are distinct conditions. The findings of this examplesuggest there may be some pathways universally altered in all PE.

In every assessment, C-RNA revealed changes in placental, fetal, andmaternally expressed transcripts. One of the most striking trendsobserved in PE samples was the increased abundance of myriad ECMremodeling and cell migration/invasion proteins (FAM107A, SLC9A3R2,TIMP4, ADAMTS1, PRG2, TIMP3, LEP, ADAMTS2, ZEB1, HSPA12B), tracking withdysfunctional extravillous trophoblast invasion and remodeling ofmaternal vessels characteristic in this disease. The maternal side ofearly-onset PE manifests as cardiovascular dysfunction, inflammation,and preterm birth (PNMT, ZEB1, CRH), all of which show molecular signsof aberrant behavior in the data of this example.

TABLE 5 PEARL Control Cohort Gestational Age Distribution for 45 healthypregnancies Control Follow up Follow up Follow up Groups Recruitmentvisit #1 visit #2 visit #3 Group 1 (n = 15) 11^(0/7)-13^(6/7) weeks14^(0/7)-17^(6/7) weeks 26^(0/7)-28^(6/7) weeks 35^(0/7)-37^(6/7) weeksGroup 2 (n = 15) 11^(0/7)-13^(6/7) weeks 18^(0/7)-21^(6/7) weeks29^(0/7)-31^(6/7) weeks 35^(0/7)-37^(6/7) weeks Group 3 (n = 15)11^(0/7)-13^(6/7) weeks 22^(0/7)-25^(6/7) weeks 32^(0/7)-34^(6/7) weeks35^(0/7)-37^(6/7) weeks

TABLE 6 Diagnostic Criteria for Preeclampsia with Severe Features andInclusion/Exclusion Criteria Blood 1) Systolic BP ≥160 mmHG or diastolicBP ≥110 mmHg measured on at Pressure least 2 occasions 4 hours apartwhile on bedrest but before the onset of labor or measured on 1 occasiononly, if antihypertensive therapy is initiated due to severehypertension Measured by one of the following: Proteinuria 1) Excretionof ≥300 mg of protein in a 24 hr period 2) Protein/creatinine value ofat least 0.3 3) qualitative determination with urine dipstick of ≥1+ ORBlood Pressure 1) Systolic BP ≥140 mmHg or diastolic ≥90 mmHG With oneof the 1) Thombocytopenia (<100,000 p1atelets/mL) following features 2)Impaired liver function 3) Newly developed renal insufficiency 4)Pulmonary edema 5) New onset cerebral disturbances or scotomataPreeclampsia Cohort Inclusion Criteria Exclusion Criteria 1. Women 18years of age or older 1. Known Malignancy 2. Pregnant women with aviable singleton 2. History of maternal organ or bone marrow gestationtransp1ant 3. Gestational age between 20 0/7 and 33 3. Maternal bloodtransfusion in the last 8 6/7 weeks determined by ultrasound weeksand/or LMP per ACOG guidelines. 4. Chronic Hypertension diagnosed priorto 4. Preeclampsia diagnosed with severe current pregnancy features perACOG guidelines 5. Type I, II or gestational diabetes 6. Fetal anomalyor known chromosome abnonnality 7. Active Labor Control Cohort InclusionCriteria Exclusion Criteria 1. Women 18 years of age or older 1. KnownMalignancy 2. Pregnant women with a viable singleton 2. History ofmaternal organ or bone marrow gestation transp1ant 3. Gestational agebetween 20 0/7 and 33 3. Maternal blood transfusion in the last 8 6/7weeks determined by ultrasound weeks and/or LMP per ACOG guidelines. 4.Chronic Hypertension diagnosed prior to current pregnancy 5. Type I, IIor gestational diabetes 6. Fetal anomaly or known chromosome abnormality7. Active Labor 8. Thrombocytopenia (<100,000 plts/mL) 9. Impaired liverfunction 10. Newly developed renal insufficiency (serum creatine >1.1mg/dL) 11. Pulmonary edema 12. New Onset cerebral disturbances orscotomata 13. Preeclampsia in prior or current pregnancy 14. Fetalgrowth restriction

TABLE 7 Study characteristics for Illumina Preeclampsia Cohort EarlyOnset PE Cohort Control Cohort Sample Size n = 40 n = 73 Gestational Ageat Sample 30.5 (+/−2.6) 30.5 (+/−2.6) Collection (weeks · days) MaternalCharacteristics Ethnicity (% Hispanic) 35% (n = 14) 41.1% (n = 30) Race% Caucasian 35% (n = 14) 46.6% (n = 34) % African 27.5% (n = 11) 17.8%(n = 13) American % Asian 7.5% (n = 3) 13.7% (n = 10) % Unknown 30% (n =12) 20.5% (n = 15) % Other 0.0% 1.4% (n = 1) Maternal Age (years, mean+/− SD) 30.4 (+/−5.7) 29.7 (+/−5.3) Maternal BMI (kg/m2, mean +/− SD)34.2 (+/−5.8) 30.1 (+/−5.6) Gravida (% Nulliparous) 32.5% (n = 13) 38.4%(n = 28) Chronic Hypertension 0% (n = 0) 0% (n = 0) Type I, II Diabetes0% (n = 0) 0% (n = 0) Gestational Diabetes 0% (n = 0) 0% (n = 0) BirthOutcomes Gestational Age at Birth (weeks · days) 31.5 (+/−3.1) 38.9(+/−1.8) Full Term 2.5% (n = 1) 90.4% (n = 66) Preterm (<37 weeks) 97.5%(n = 39) 9.6% (n = 7) Sex (% male) 37.5% (n = 15) 42.5% (n = 31) BirthWeight (kg) 1.4 (+/−0.52) 3.2 (+/−0.55) Small for Gestational Age* 45%(n = 18) 9.6% (n = 7) Stillbirth 2.5% (n = 1) 0% (n = 0) Medications fortreatment of: PE/Hypertension MgSO4 82.5% (n = 33) 4.1% (n = 3)Antenatal Steroids 95.0% (n = 38) 6.8% (n = 5) Anti-Hypertensive 75.0%(n = 30) 5.3% (n = 4) Aspirin 20.0% (n = 8) 0% (n = 0) Other ConditionsAnalgesics 60.0% (n = 24) 11.8% (n = 9) Antimicrobials 12.5% (n = 5)5.5% (n = 4) Antihistamines 32.5% (n = 13) 13.7% (n = 10) Asthma 10.0%(n = 4) 2.7% (n = 2) Psychoactive 15.0% (n = 6) 5.5% (n = 4)Hypothyroidism 7.5% (n = 3) 2.7% (n = 2) Antiemetics 25.0% (n = 10) 5.5%(n = 4) Pregnancy Antacids 27.5% (n = 11) 8.2% (n = 6) SymptomsAnti-constipation 15.0% (n = 6) 11.8% (n = 9) Prenatal Vitamins 17.5% (n= 7) 31.5% (n = 23) Iron Supplement 10% (n = 4) 12.3% (n = 9) *Definedas birthweight <10% of population for male or female fetus

TABLE 8 Medical Center Collection Site Patient Distribution Number PENumber of Clinical Site Location patients controls University of TexasMedical Branch Galveston, Texas 4 11 Tufts Medical Center Boston, MA 1017 Columbia University Irving Medical New York, NY 4 9 Center WinthropUniversity Hospital Mineola, NY 5 9 St. Peter's University Hospital NewBrunswick, NJ 3 6 Christiana Care Newark, DE 7 13 Rutgers UniversityRobert Wood New Brunswick, NJ 5 8 Johnson Medical School New YorkPresbyterian/Queens New York, NY 2 3 Total Samples collected 40 76

TABLE 9 Genes validated by TaqMan qPCR Gene Name Assay ID Type RefSeqABHD12 Hs01018050_m1 Reference NM_001042472.2 ABHD12 Hs01018050_m1Target NM_001042472.2 ADAMTS2 Hs01029111_m1 Target NM_014244.4 ALOX15BHs00153988_m1 Target NM_001039130.1 ARHGEF25 Hs00384780_g1 TargetNM_001111270.2 ARRDC2 Hs01006434_g1 Target NM_001286826.1 CLEC4CHs01092460_m1 Target NM_130441.2 DAAM2 Hs00322497_m1 TargetNM_001201427.1 FAM107A Hs00200376_m1 Target NM_001076778.2 HSPA12BHs00369554_m1 Target NM_001197327.1 HTRA4 Hs00538137_m1 TargetNM_153692.3 IGFBP5 Hs00181213_m1 Target NM_000599.3 KRBOX4 Hs01063506_gHReference NM_001129898.1 KRT5 Hs00361185_m1 Target NM_000424.3 LEPHs00174877_m1 Target NM_000230.2 NES Hs00707120_s1 Target NM_006617.1NME3 Hs01573872_g1 Reference NM_002513.2 PAPPA2 Hs01060983_m1 TargetNM_020318.2 PITPNM3 Hs01107787_m1 Target NM_001165966.1 PLD4Hs00975488_m1 Target NM_001308174.1 PRG2 Hs00794928_m1 TargetNM_001243245.2 TIMP3 Hs00165949_m1 Target NM_000362.4 TIMP4Hs00162784_m1 Target NM_003256.3 VSIG4 Hs00907325_m1 TargetNM_001184830.1 WNT7A Hs00171699_m1 Reference NM_004625.3 ZEB1Hs01566408_m1 Target NM_001128128.2 ZNF138 Hs00864088_gH ReferenceNM_001271638.1

TABLE 10 Previous Gene Literature Change Sub-Cellular Symbol AnalysisReports in PE Tissue Expression Category Location Function(s) ARRDC2AdaBoost No Increase* Other (Skeletal Muscle; Membrane ProteinTrafficking Globus Pallidus; Lung) ALOX15B DEX Yes Increase FetalNucleus; Cytoskeleton; Cell Cycle; Cytosol; Membrane Immune Function;Cardiovascular Function AMPH DEX No Increase Fetal Cytoskeleton;Synaptic Vesicle Endocytosis Membrane CUX2 DEX No Decrease Fetal NucleusCell Cycle; Fetal Development; DNA Damage Response FAM107A DEX NoIncrease Fetal Cytoskeleton; Cell Migration/Invasion; Membrane; CellCycle; ECM Regulation Nucleus IGFBP5 DEX Yes Increase FetalExtracellular Fetal Development; IGF Signaling or Secreted NES DEX YesIncrease Fetal Cytoskeleton Fetal Development; Cell Cycle PITPNM3 DEX NoIncrease Fetal Membrane Phosphatidylinositol Regulation PRX DEX YesIncrease Fetal Membrane Cell Structure/Composition TEAD4 DEX YesIncrease Fetal Nucleus Placental Development PNMT DEX Yes Increase Other(Adrenal Cytosol Epinephrine Synthesis; Gland Cortex; CardiovascularFunction; Adrenal Gland; Pregnancy Duration Skeletal Muscle Psoas) DAAM2DEX Yes Increase Other (Corpus Callosum; Extracellular Fetal DevelopmentGlobus Pallidum External; or Secreted Nodose Nucleus) SLC9A3R2 DEX NoIncrease Other (Heart Ventricle; Membrane; Nucleus ECM Regulation;Liver; Parotid Gland) Cell Structure/Composition HSPA12B DEX No IncreaseOther (Heart Ventricle; unknown Angiogenesis; Cardiovascular Function;Lung; Spleen) Cell Migration/Invasion; Hypoxia Response PLD4 DEX NoDecrease Other (Nodose Nucleus; Membrane PhosphatidylinositolSubthalamic Nucleus; Regulation; Immune Function Corpus Callosum) TIMP4DEX No Increase Other (Omental Extracellular ECM Regulation; ImmuneFunction Adipose Tissue; or Secreted Subcutaneous Adipose Tissue; JointSynovium) KRT5 DEX Yes Decrease Other (Oral Mucosa; Cytoskeleton CellStructure/Composition Pharyngeal Mucosa; Esophagus) ZEB1 DEX No IncreaseOther (Synovial Membrane; Nucleus Immune Function; CellMigration/Invasion; Aorta; Myometrium) Fetal Development; PregnancyDuration APOLD1 DEX Yes Increase Placental Plasma Membrane Angiogenesis;Cardiovascular Function; Hypoxia Response; Fetal Development HTRA4 DEXYes Increase Placental Extracellular IGF Signaling; PlacentalDevelopment or Secreted SEMA3G DEX No Increase Placental ExtracellularCell Migration/Invasion or Secreted ADAMTS1 DEX Yes IncreasePlacental/Fetal Extracellular ECM Regulation; or Secreted FetalDevelopment; Angiogenesis CRH DEX Yes Increase Placental/FetalExtracellular Pregnancy Duration; or Secreted Fetal Development;Cardiovascular Function PRG2 DEX Yes Increase Placental/FetalExtracellular Immune Function; or Secreted ECM Regulation; IGF SignalingTIMP3 DEX Yes Increase Placental/Fetal Extracellular ECM Regulation; orSecreted Immune Function; Angiogenesis ARHGEF25 DEX & No Increase Other(Hippocampus; Membrane; Sarcomere AdaBoost Myometrium; Cerebellum)CLEC4C DEX & Yes Decrease Other (Rectum Colon; Membrane Immune FunctionAdaBoost Ascending Colon; Substantia Nigra Reticulata) LEP DEX & YesIncrease Placental Extracellular Energy Homeostasis; AdaBoost orSecreted Immune Function; Angiogenesis; Fetal Development; ECMRegulation PAPPA2 DEX & Yes Increase Placental Extracellular FetalDevelopment; AdaBoost or Secreted IGF Signaling VSIG4 DEX & Yes IncreasePlacental Membrane Immune Function AdaBoost ADAMTS2 DEX & No IncreasePlacental/Fetal Extracellular ECM Regulation; AdaBoost or SecretedAngiogenesis; Fetal Development Key Increase* indicates the change wasnot statistically different CorrelationEngine Body Atlas was used tofind the 3 top tissues expressing genes in the “Other” category UniProtwas used to determine sub-cellular localization . . . as a note; Imerged all “membrane” classifications to one category (so PlasmaMembrane; ER Membrane; etc are not distinct)

TABLE 11 Study characteristics for Illumina Preeclampsia Cohort EarlyOnset Early Onset Late Onset Late Onset PE Control PE Control SampleSize n = 12 n = 12 n = 12 n = 12 Gestational Age at Sample 29.2 (+/−2.3)29.3 (+/−2.3) 35.6 (+/−1.3) 35.9 (+/−0.8) Collection (weeks · days)Maternal Characteristics Ethnicity (% Hispanic) 0% (n = 0) 0% (n = 0) 0%(n = 0) 0% (n = 0) Race % Caucasian 91.7% (n = 11) 100% (n = 12) 100% (n= 12) 100% (n = 12) % African 8.3% (n = 1) 0% (n = 0) 0% (n = 0) 0% (n =0) Maternal Age (years, mean +/− SD) 29.3 (+/−3.5) 30.1 (+/−3.8) 30.2(+/−4.8) 29.4 (+/−3.2) Maternal BMI (kg/m2, mean +/− SD) 33.6 (+/−9.0)28.5 (+/−7.0) 32.2 (+/−4.9) 27.9 (+/−4.5) Gravida (% Nulliparous) 60% (n= 6) 58.3% (n = 7) 75% (n = 9) 75% (n = 9) Chronic Hypertension 13.3% (n= 2) 8.3% (n = 1) 8.3% (n = 1) 0% (n = 0) Type I, II Diabetes 13.3% (n =2) 0% (n = 0) 25.0% (n = 3) 0% (n = 0) Gestational Diabetes 13.3% (n =2) 33.3% (n = 4) 8.3% (n = 1) 16.7% (n = 2) Other Health Condition 0% (n= 0) 8.3% (n = 1) 0% (n = 0) 0% (n = 0) Birth Outcomes Gestational Ageat Birth (weeks · days) 30.3 (+/−3.4) 39.0 (+/−1.5) 37.0 (+/−1.4) 39.7(+/−1.6) Full Term 0% (n = 0) 91.7% (n = 11) 75.0% (n = 9) 91.7% (n =11) Preterm (<37 weeks) 100% (n = 12) 8.3% (n = 1) 25.0% (n = 3) 8.3% (n= 1) Sex (% male) 75% (n = 9) 58.3% (n = 7) 66.7% (n = 8) 58.3% (n = 7)Birth Weight (kg) 1.3 (+/−0.54) 3.2 (+/−0.40) 2.7 (+/−0.55) 3.4(+/−0.54) Fetal Growth Restriction 50.0% (n = 6) 0% (n = 0) 8.3% (n = 1)0% (n = 0) Small for Gestational Age* 25% (n = 3) 0% (n = 0) 33.3% (n =4) 25% (n = 3) Stillbirth 0% (n = 0) 0% (n = 0) 0% (n = 0) 0% (n = 0)HELLP 25.0% (n = 3) 0% (n = 0) 0% (n = 0) 0% (n = 0) Medications fortreatment of: PE/Hypertension MgSO4 83.3% (n = 10) 0% (n = 0) 33.3% (n =4) 0% (n = 0) Antenatal Steroids 100% (n = 12) 0% (n = 0) 25.0% (n = 3)0% (n = 0) Anti-Hypertensive 100% (n = 12) 8.3% (n = 1) 75% (n = 9) 0%(n = 0) Aspirin 8.3% (n = 1) 25.0% (n = 3) 25.0% (n = 3) 8.3% (n = 1)*Defined as birthweight <10% of population for male or female fetus

Example 7 Circulating Transcriptome Measurements from Maternal BloodDetect a Molecular Signature of Early-Onset Preeclampsia

Circulating RNA (C-RNA) is continually released into the bloodstreamfrom tissues throughout the body, offering an opportunity tonon-invasively monitor all aspects of pregnancy health from conceptionto birth. This example determines that C-RNA analysis can detectaberrations in patients diagnosed with preeclampsia (PE), a prevalentand potentially fatal pregnancy complication. As an initial examination,the circulating transcriptome from 40 pregnancies was sequenced at thetime of severe, early-onset PE diagnosis along, with 73 gestationalage-matched controls. 30 transcripts consistent with the biology of PEwere altered, and likely represent placental, fetal, and maternalcontributions to the disease. Further, machine learning identified acombination of C-RNA transcripts which robustly classified PE patientsin two independent cohorts (85%-89% accuracy). The ability of C-RNA toreflect maternal, placental and fetal health holds great promise forimproving the diagnosis and identification of at-risk pregnancies. Insummary, the circulating transcriptome reflects biologically relevantchanges in patients with early-onset severe preeclampsia and can be usedto accurately classify patient status.

Preeclampsia (PE) is one of the most common and serious complications ofpregnancy, affecting an estimated 4-5% of pregnancies worldwide (Abaloset al., 2013, Eur J Obstet Gynecol Reprod Biol; 170:1-7; and Ananth etal., 2013, BMJ; 347:f6564) and is associated with substantial maternaland perinatal morbidity and mortality (Kuklina et al., 2009, ObstetGynecol; 113:1299; and Basso et al., 2006, AMA; 296:1357-1362). In theUnited States, the incidence of PE is increasing due to advancedmaternal age and the increasing prevalence of comorbid conditions suchas obesity (Spradley et al., 2015, Biomolecules; 5:3142-3176), costingthe US healthcare system an estimated 2 billion dollars annually(Stevens et al., 2017, Am J Obstet Gynecol; 217:237-248.e16).

PE is diagnosed as new-onset hypertension accompanied by maternalend-organ damage occurring after 20 weeks' gestation (Hypertension inPregnancy: Executive Summary, 2013, Obstet Gynecol; 122:1122; andTranquilli et al., 2014, Pregnancy Hypertens; 4(2): 97-104). However,there is significant heterogeneity in the presentation and progressionof PE, including timing of disease onset, symptom severity, clinicalmanifestations, and maternal and neonatal outcomes (Lisonkova andJoseph, 2013, Am J Obstet Gynecol; 209:544.e1-544.e12). PE is primarilydelineated based on whether it manifests before (early-onset) or after(late-onset) 34 weeks and if it presents with severe features, such assustained elevation in blood pressure ≥160/110 mmHg, neurologicalsymptoms, and/or severe liver or kidney injury (American College ofObstetricians and Gynecologists, Task Force on Hypertension inPregnancy, 2013, Obstet Gynecol; 122:1122-1131).

The pathophysiology of early-onset PE is incompletely understood, but isthought to occur in two phases (Phipps et al., 2019, Nature ReviewsNephrology; 15:275). Early-onset PE originates with abnormalimplantation and placentation in the first trimester, related tomaternal immune dysfunction (Hiby et al., 2010, J Clin Invest;120:4102-4110; Ratsep et al., 2015, Reproduction; 149:R91-R102; andGirardi, 2018, Semin Immunopathol; 40:103-111), incompletecytotrophoblast differentiation (Zhou et al., 1997, J Clin Invest;99:2152-2164), and/or oxidative stress at the maternal-placentalinterface (Burton and Jauniaux, 2011, Best Pract Res Clin ObstetGynaecol; 25:287-299), resulting in incomplete remodeling of thematernal spiral arteries and failure to establish the definitiveuteroplacental circulation (Lyall et al., 2013, Hypertension;62:1046-1054). This leads to inadequate placental perfusion after 20weeks' gestation. The resultant placental dysregulation triggers phasetwo, which manifests predominantly as maternal systemic vasculardysfunction with negative consequences for the fetus, including fetalgrowth restriction and iatrogenic preterm birth (Hecht et al., 2017,Hypertens Pregnancy; 36:259-268; Young et al., 2010, Annu Rev Pathol;5:173-192; and Backes et al., 2011, J Pregnancy;2011:doi:10.1155/2011/214365. In contrast, the placental dysfunction inlate-onset PE is thought to be due not to abnormal placentation, but todisturbance in placental perfusion resulting from maternal vasculardisease, such as that seen in patients with chronic hypertension,pregestational diabetes (Vambergue and Fajardy, 2011, World J Diabetes;2:196-203), and collagen vascular disorders (“Placental pathology inmaternal autoimmune diseases-new insights and clinical implications,”2017, International Journal of Reproduction, Contraception, Obstetricsand Gynecology; 6:4090-4097).

The heterogeneity and complexity of this disease have made it difficultto diagnose, to predict risk, and to develop treatments. Further, theinability to easily interrogate the primary affected organ, theplacenta, has limited molecular characterization of disease progression.Circulating RNA (C-RNA) has shown great promise for non-invasivemonitoring of maternal, placental and fetal dynamics during pregnancy(Tsui et al., 2014, Clin Chem; 60:954-962; and Koh et al., 2014, ProcNatl Acad Sci USA; 111:7361-7366). C-RNA is released by many tissuesinto the bloodstream via multiple cellular processes of apoptosis,microvesicle shedding, and exosomal signaling (van Niel et al., 2018,Nat Rev Mot Cell Biol; 19:213-228). Due to these diverse origins, C-RNAmeasurements reflect tissue-specific changes in gene expression,intercellular signaling, as well as the degree of cell death occurringwithin different tissues throughout the body. Thus, C-RNA has thepotential to elucidate the molecular underpinnings of PE and ultimatelyidentify predictive, prognostic and diagnostic biomarkers of the disease(Hahn et al., 2011, Placenta; 32:S17-20).

Several studies have begun to investigate and identify potentialC-RNA-based biomarkers for a range of pregnancy complications, includingpreterm birth, PE, and infectious disease (Pan et al., 2017, Clin Chem;63:1695-1704; Ngo et al., 2019, Science; 360:1133-1136; and Whitehead etal., 2016, Prenat Diagn; 36:997-1008). However, the significantinterindividual variability in this sample type threatens to obscuresubtle changes in disease specific biomarkers (Meder et al. 2014, ClinChem; 60:1200-1208). Therefore, many PE-focused C-RNA studies havemeasured RNA of previously identified serum protein biomarkers,including soluble FLT1, soluble endoglin, and oxidative stress andangiogenic markers (Nakamura et al., 2009, Prenat Diagn; 29:691-696;Purwosunu et al., 2009, Reprod Sci; 16:857-864; and Paiva et al., 2011,J Clin Endocrinol Metab; 96:E1807-1815). While protein measurements ofthese serum markers are known to be altered in PE (Maynard et al., 2003,J Clin Invest; 111:649-658; Venkatesha et al., 2006, Nat Med;12:642-649; and Rana et al., 2018, Pregnancy Hypertens; 13:100-106), itis unknown whether they will serve as the most effective predictors inC-RNA, thus warranting a broader discovery approach.

In this example, global measurements of the circulating transcriptomedetect unique molecular signatures specific to early-onset severe PE. Tofacilitate discovery, the performance of whole transcriptome enrichmentfor high throughput sequencing was optimized, allowing for measurementof >14,000 C-RNA transcripts per sample with high confidence. C-RNAprofiles were then globally characterized from a preliminary cohort of113 pregnancies, 40 of which were diagnosed with early-onset severe PE.All analysis methods were tailored to address the high biologicalvariance inherent to C-RNA and identified altered transcripts concordantwith PE biology that can classify across cohorts with high accuracy,highlighting that this sample type offers an avenue to developing robusttests for assessing preeclampsia.

Results

Establishing a reproducible whole-transcriptome workflow for C-RNA.

C-RNA is present in plasma in relatively low abundance, dominated byribosomal (rRNA) and globin RNA, and is a mixture of fragmented andfull-length transcripts (Crescitelli et al., 2013, J Extracell Vesicles;2:doi:10.3402/jev.v2i0.20677), all of which can affect the efficiency oflibrary preparation methods for next generation sequencing. Thus,workflow was optimized to minimize variability and maximize exonic C-RNAsignal (FIG. 37A).

Highly abundant globin and rRNA does not inform biomarker discovery andmust be removed. However, standard depletion methods such as Ribo-Zero(Illumina, Inc) or NEBNext rRNA Depletion (New England Biolabs) are notwell suited for low starting amounts of RNA (Adiconis et al., 2013, NatMethods; 10:623-629). While upfront depletion with these methodssuccessfully removed unwanted ribosomal sequences from C-RNA (FIG. 37B,gray), sequencing libraries did not consistently exhibit an increase inexonic C-RNA signal (FIG. 37B, orange). Instead, samples varied in theproportion of reads mapping to a complex and variable population ofnon-human RNA sequences such as GB Virus C (FIG. 37B, pink) (Manso etal., 2017, Sci Rep; 7:doi:10.1038/s41598-017-02239-5; and Whittle etal., 2019, Front Microbiol; 9:doi:10.3389/fmicb.2018.03266).Additionally, removal of highly abundant rRNA and globin RNA results inextremely low RNA inputs which increases the failure rate ofligation-based library preparation methods. To avoid these problems, awhole-transcriptome enrichment approach was selected which generates alibrary from all C-RNA followed by probe-assisted enrichment targetingthe whole human exome (FIG. 37A). This method consistently generatedhigh quality sequencing libraries that were composed of >90% exonicC-RNA with minimal contaminating signal from transcripts of limitedinterest (FIG. 37B, orange).

There is significant interindividual variability in C-RNA plasmaconcentrations (FIG. 38A), which can vary by an order of magnitude(average 1.1 ng/mL plasma; SD 0.7; range <0.1-5 ng/mL plasma). To ensurereproducible results, the effects of plasma volume input on C-RNA dataquality was evaluated. Using less than 2 mL plasma significantlyincreased the biological coefficient of variation and decreased librarycomplexity (FIGS. 38B and 38C), leading to a decrease in sensitivity.Therefore, a 4 mL plasma input was selected to minimize noise, maximizeconfidence in data quality and to ensure successful data generation fromall individuals regardless of C-RNA plasma concentrations.

To achieve consistent handling of all samples across diverse collectionsites, all processing was centralized and required shipping of thecollected blood samples to a single lab. This necessitated an assessmentof the impact of overnight shipping on C-RNA data quality. Blood fromnonpregnant and pregnant women (gestational age, GA, >28 weeks) wascollected in four blood collection tubes (BCTs) and stored overnight atthe manufacturer-indicated temperature prior to processing: BDVacutainer K₂EDTA (Beckton Dickinson; 4° C.), BD Vacutainer ACD-A(Beckton Dickinson, 4° C.), Cell-Free DNA BCT (Streck, Inc., RT),Cell-Free RNA BCT (Streck, Inc., RT). A set of samples collected in EDTABCTs was processed at the clinic within 2 hours to provide a baseline.The C-RNA pregnancy signal in each sample was measured by summing thenormalized abundance level of 155 transcripts identified as pregnancymarkers in two prior C-RNA publications (Tsui et al., 2014, Clin Chem;60:954-962; and Koh et al., 2014, Proc Natl Acad Sci USA;111:7361-7366). After overnight storage, the C-RNA pregnancy signal wasclearly detectable in pregnant samples despite a reduction in overallsignal intensity as compared to immediate processing of the EDTA samples(FIGS. 39A and 39B). No major differences were observed between thedifferent BCTs after overnight storage, indicating all are appropriatefor C-RNA analysis. Cell-Free DNA BCTs (Streck, Inc) were selected forsubsequent sample collections as this enables room temperature shipping.Furthermore, correlation of transcriptomic profiles in a time courseexperiment confirmed that there is no increase in technical varianceafter room temperature storage up to five days in these BCTs (FIG. 39C).

The complete workflow was validated by recapitulating previous workmonitoring C-RNA dynamics in healthy pregnancies from first to thirdtrimester. Using 152 samples collected serially from 41 healthypregnancies (Pre-Eclampsia and Growth Restriction Longitudinal StudyControl Cohort—PEARL CC; NCT02379832; Table 13), 156 significantlyaltered transcripts were identified, with the majority increasing inabundance as pregnancy progresses (FIG. 32A; Table 14). 51% ofidentified transcripts changed primarily during the first trimester, 6%in the 3^(rd) trimester, and 43% were differentially regulatedthroughout gestation (FIG. 40 ; Table 14). First trimester genes wereenriched for placental steroidogenesis and regulation of trophoblastdifferentiation, while third trimester genes were involved in the onsetof labor. Transcripts that increase throughout gestation are associatedwith tissue and organ development and morphogenesis (Chatuphonprasert etal., 2018, Front Pharmacol; 9:doi:10.3389/fphar.2018. 01027; Debieve etal., 2011, Mol Hum Reprod; 17:702-9; Grammatopoulos and Hillhouse, 1999,Lancet; 354:1546-1549; and Marshall e al., 2017, Reprod Sci;24:342-354). The results from the PEARL CC were highly concordant withthe literature, as 42% of the altered genes were identified in priorC-RNA studies (FIG. 32B) (Tsui et al., 2014, Clin Chem; 60:954-962; andKoh et al., 2014, Proc Natl Acad Sci USA; 111:7361-7366). Of the 91transcripts identified only in this study, 64% are expressed byplacental and/or fetal tissues (tissue specificity defined as >2 foldhigher than median of all tissues in Body Atlas) (FIG. 32C and FIG. 17)(Kupershmidt et al., 2010, PLOS ONE; 5:e13066). The remaining genes arehypothesized to reflect maternal tissue responses to pregnancy (Table14).

Clinical Study Design for Early-Onset Severe PE

After confirming this workflow robustly detects pregnancy-related C-RNAdynamics, changes in C-RNA associated with pregnancy complications werethen identified. The workflow was applied to samples collected from twoindependent PE cohorts, the Illumina Preeclampsia Cohort (iPEC;NCT02808494) and the PEARL Preeclampsia Cohort (PEARL PEC; NCT02379832).The iPEC was used for biomarker identification while the PEARL PEC wasused for independent confirmation of our findings. Importantly, allsamples in both cohorts were collected in Streck Cell-Free DNA BCTs andlibraries were generated in the same manner as discussed in the previoussection.

The iPEC study focused on early-onset PE with severe features andexcluded women diagnosed with additional health complications such aschronic hypertension or diabetes, to prevent additional heterogeneityfrom obscuring a consistent PE-associated C-RNA signal (Table 15). 113samples were collected across 8 sites (Table 17), 40 at the time ofearly-onset PE diagnosis, and 73 controls that were gestationalage-matched to within 1 week (FIG. 33A). Maternal characteristics,pregnancy outcomes, and medications were recorded throughout the study(Table 12 and Table 16). Fetal gender, maternal age, and nulliparitywere not significantly different between the PE and control groups. Incontrast, BMI was significantly higher in the PE cohort, (pvalue=0.0007) (O'Brien et al., 2003, Epidemiology; 14:368-374). All butone patient with PE gave birth prematurely, in contrast to 9.5% ofcontrols, confirming that our diagnostic criteria identified individualsseverely impacted by this disease (FIG. 33C).

The PEARL PEC samples were collected by an independent institution (CHUde Québec-Université Laval) and consisted of 12 early- and 12 late-onsetPE pregnancies with equal numbers of gestational age-matched controls(FIG. 33B). Maternal characteristics, pregnancy outcomes, andmedications in use were recorded throughout the study (Table 18). As iniPEC, 100% of early-onset patients delivered prematurely while 75%delivered at term in the late-onset cohort, confirming the differencesin severity associated with early- and late-onset PE. Chronichypertension, diabetes and other maternal health conditions were notgrounds for exclusion, making this cohort more representative of theheterogeneity inherent to the pregnant population.

Identification of Transcripts Consistently Altered in Early-Onset PE

Standard differential expression analysis (Robinson et al., 2010,Bioinformatics; 26:139-140) using the full iPEC cohort identified 42transcripts with altered abundance in plasma, 37 of which were increasedin PE (FIG. 34A, blue and orange). However, variability in thedifferentially abundant transcripts was observed when different subsetsof samples were selected for analysis. A jackknifing approach (Library,1958, Ann Math Statist; 29:614-623) was therefore incorporated, enablingthe identification of transcript abundances that are most consistentlyaltered when comparing PE to control samples (FIGS. 34A and 34B,orange). One thousand iterations of differential analysis with randomlyselected PE and control sample subsets were performed, resulting in theconstruction of confidence intervals for the p-values associated witheach putatively altered transcript (FIG. 34C). Twelve transcripts whosep-value confidence interval exceeded 0.05 were subsequently excluded(FIG. 34B). These transcripts would not have been excluded by simplysetting a threshold for baseline abundance or biological variance (FIG.34D), however these transcripts were observed to have lower predictivevalue (FIG. 34E). Hierarchical clustering indicates these transcriptsare not universally altered in the PE cohort, and thus lack sensitivity(73%) for accurate classification of this condition (FIG. 34F).

A representative set of 20 transcripts altered in PE were independentlyquantified by qPCR in a subset of affected and control iPEC patientsamples. Fold changes measured by qPCR were highly concordant with thesequencing data, validating our findings (FIG. 35A and Table 19). 58% ofthe transcripts in the refined list have previously been associated withPE (Table 20). Additionally, nearly all genes can be linked to PErelevant processes, including extracellular matrix (ECM) remodeling,pregnancy duration, placental/fetal development, angiogenesis, andhypoxia response (Table 20). 67% of these genes were expressed by theplacenta and/or fetus (FIG. 35B). In the remaining maternally expressedgenes, cardiovascular and immune functions were well represented, bothof which are altered in PE (Table 20) (Phipps et al., 2019, NatureReviews Nephrology; 15:275).

Hierarchical clustering of these transcripts effectively segregated PEand control samples from iPEC with 98% sensitivity and 97% specificity(FIG. 35C). The refined list of 30 transcripts was validated with theindependent PEARL PEC. Early-onset PE samples clustered separately frommatched controls with 83% sensitivity and 92% specificity, furthervalidating the relevance of these transcripts (FIG. 35D). In contrast,no clustering was observed for the late-onset PE and matched controlsamples indicating that late-onset PE has a potentially weaker or morelikely a different C-RNA signature (FIG. 35E) (Redman, 2017, AnInternational Journal of Women's Cardiovascular Health; 7:58; and Hahnet al., 2015, Expert Rev Mol Diagn; 15:617-629).

Upon closer inspection of the clinical data for the three misclusterediPEC samples, it was discovered that two controls suffered frompotentially confounding health problems, including hypertension and inone case accompanied by preterm delivery. These controls should not havebeen enrolled in the iPEC due to our stringent exclusion criteria, thusthey were excluded from further analyses. The misclustered PE sampleshowed no clinical abnormalities and was retained in our iPEC dataset.

Development of a Robust Machine Learning Classifier for Early-Onset PE

Differential expression analysis confirmed that C-RNA detectsbiologically relevant changes in PE patients. To assess if C-RNAsignatures can robustly classify PE, the data from the iPEC cohort wereused to build an AdaBoost model (Freund and Schapire, 1997, J Comp SysSci; 55:119-139; McPherson et al., 2011, PLoS Comput Biol;7:doi:10.1371/journal.pcbi.1001138; and Lu et al., 2015, PLOS ONE;10:e0130622). A randomly selected 10% of samples were excluded as aholdout set from the entire machine learning process to assess the finalmodel performance. Then, a nested cross-validation approach was used forhyperparameter optimization (FIG. 42 ) and AdaBoost model building (FIG.43 ) (Cawley and Talbot, 2010, J Machine Learning Res; 11:2079-2107).

While developing the machine learning approach, a high degree ofvariability was observed in AdaBoost performance and in the genesselected depending on which samples were included in training (FIG. 44). These observations indicate that different subsets of samplessignificantly impact model construction (Assessing and improving thestability of chemometric models in small sample size situations |SpringerLink (available on the world wide web atlink.springer.com/article/10.1007%2Fs00216-007-1818-6)) which is likelydue in part to the heterogeneity of PE. To account for this diversity,AdaBoost models were fit to multiple combinations of the trainingsamples. The estimators from these orthogonally generated models werethen combined into a single aggregate and pruned to obtain a minimalgene set (FIG. 43 ) (Martinez-Munoz and Suarez, 2007, PatternRecognition Letters; 28:156-165; and AveBoost2: Boosting for Noisy Data| SpringerLink ((available on the world wide web atlink.springer.com/chapter/10.1007/978-3-540-25966-4_3))). This allowedthe capture of wide diversity of PE manifestations in a refined machinelearning model with the potential to accurately classify independentsamples from a broad pregnancy population.

Within each fold of the cross-validation, a threshold AdaBoost score wasidentified for discriminating PE and control samples which maximizedboth sensitivity and specificity. Across all ten folds, we obtained anaverage ROC of 0.964 (+/−0.068 SD) (FIG. 36A). Performance was firstassessed in the holdout iPEC samples obtaining 89% (+/−5% SD) accuracy,with 88% (+/−13% SD) sensitivity and 92% (+/−6% SD) specificity (FIG.36B, blue). AdaBoost classification performance was not affected by theamount of time prior to plasma processing, further supporting therobustness of our sample preparation protocol and analyses (FIGS. 40Dand 40E). The model's capabilities to classify the independent PEARL PECcohort were subsequently investigated. Early-onset PEARL PEC samplesachieved 85% (+/−4% SD) accuracy, with 77% (+/−9% SD) sensitivity and92% (+/−7% SD) specificity (FIG. 36B, pink). Unexpectedly, late-onsetPEARL PEC samples were also classified with a reasonable accuracy of 72%(+/−6% SD), sensitivity of 59% (+/−10% SD) and specificity of 80%(+/−10% SD) (FIG. 36B, green).

49 total transcripts were used by AdaBoost, with 63% selected in atleast 2 rounds of model building (FIG. 36C). Concordance was observedwith prior analyses, as 40% of the genes identified in the jackknifinganalysis were also used in machine learning (FIG. 36D, Table 21). 38% ofthe transcripts used by the classifiers have elevated expression in theplacenta and/or fetus (FIG. 36E). Transcripts reflecting a diversity ofPE-relevant pathways were observed, particularly genes associated withimmune regulation and fetal development (Table 21).

Discussion

Whole transcriptome C-RNA analysis casts the wide net necessary foreffective biomarker discovery, capturing a molecular snapshot of thediverse and complex interactions of pregnancy at a single point in time.Workflow and analyses were tailored to minimize technical noise, obtainhigh quality C-RNA measurements, and ultimately detect biologicallyrelevant alterations. Importantly, molecular changes were detectedspecific to the complex pathophysiology of early-onset severe PE at thetime of diagnosis, supporting robust classification across cohorts. Thealtered C-RNA transcripts identified represent contributions frommaternal, placental and fetal tissues, many of which would not becaptured in studies focusing on placental tissues collected afterdelivery. These discoveries highlight the power of C-RNA tocomprehensively monitor signals contributed by diverse tissues of originwhile the pregnancy is ongoing.

To identify the best method capable of detecting global and potentiallysubtle changes in pregnancy, the effects of plasma input, librarypreparation methods, and BCTs on C-RNA data quality were determined. Asthe majority of transcripts appear to be present in plasma at lowabundance, the use 4 mL plasma inputs was chosen in protocols tominimize noise due to sampling error and poor library conversion, bothof which plague low input sequencing applications. Upfront depletion ofabundant RNA did not eliminate all contaminating RNA species, which werenumerous and diverse in our sample population. This made targeteddepletion infeasible, thus we selected a whole transcriptome enrichmentapproach to consistently isolate the exonic C-RNA signal of interest.Overnight shipping was a logistical requirement of the protocol, butruns the risk of introducing both signal loss due to C-RNA degradationas well as contamination with additional RNA from cell lysis. StreckCell Free DNA BCTs, which inhibit cell lysis at room temperature (Zhaoet al., 2019, J Clin Lab Anal; 33:e22670), were selected. This BCT isnot specifically designed for RNA stabilization, but little evidence ofC-RNA degradation was observed after storage for several days. Previousstudies have shown that C-RNA has sufficient endogenous protection fromextracellular nucleases (Tsui et al., 2002, Clin Chem; 48:1647-1653)thus additional precautions to protect the RNA are unnecessary.Together, these optimizations identified a workflow that maximizes C-RNAtranscriptomic signal and minimizes technical variability, asillustrated by the numerous biologically relevant alterations observedacross healthy and PE pregnancies.

Analyses next focused our analyses on identifying differences incirculating transcriptomes that are ubiquitous to the most extremephenotype of the disorder, namely early-onset PE with severe features.This required tailoring our approach to account for the variabilityobserved in our data that stems from both the substantial biologicalnoise in C-RNA measurements as well as the phenotypic diversity of PE.C-RNA is inherently more variable than single tissue transcriptomics,because it interrogates RNA from diverse tissues and biologicalprocesses, not only detecting changes in gene expression but alsodifferences in the rates of cell death and intercellular signaling.Furthermore, PE exhibits a wide range of maternal and fetalmanifestations and outcomes, which may be associated with differentunderlying molecular causes and responses. While the genes eliminatedafter jackknifing may be biologically relevant in PE, they were notuniversally altered in the affected cohort. These transcripts mayindicate a molecular subset of the disease and larger cohorts will helpelucidate whether C-RNA can further delineate PE subtypes, which iscrucial to understanding the diverse pathophysiology of this syndrome.

The transcripts identified by jackknifing represent a diversity offunctions spanning the maternal-fetal interface. A majority of theidentified changes relate to placental dysfunction and altered fetaldevelopment. One of the most striking trends was an increased abundanceof ECM remodeling and cell migration proteins (N=10), tracking withdysfunctional extravillous trophoblast invasion characteristic ofearly-onset PE (Yang et al., 2019, Gene; 683:225-232; Zhu et al., 2012,Rev Obstet Gynecol; 5:e137-e143; and Wang et al., 2019, ScientificReports; 9:2728). 20% of dysregulated genes identified encode forangiogenic proteins, consistent with a number of observations that thebalance of angiogenic factors play a crucial role in regulatingplacental vascular development (Cerdeira et al., 2012, Cold SpringHarbor Perspectives in Medicine; 2:a006585-a006585) and can identifyearly-onset PE with severe features (Zeisler et al., 2016, N Engl J Med;374:13-22). The data presented here indicated that fetal growth anddevelopment was also perturbed in early-onset severe PE, as evidenced byincreased abundance of 4 transcripts encoding regulators of IGFsignaling (Argente et al., 2017, EMBO Mol Med; 9:1338-1345; and Weyerand Glerup, 2011, Biol. Reprod; 84:1077-1086), a critical pathway forfetal development (Forbes and Westwood, 2008, Horm Res; 69(3):129-137).The remaining transcripts captured the maternal component of PE, namelyimmune and cardiovascular system dysregulation. Evidence of maternalimmune imbalance, a hallmark of PE, appeared as altered abundance ofimmunological tolerance and pro- and anti-inflammatory factors(Chistiakov et al., 2014, Front Physiol; 5 (2014),doi:10.3389/fphys.2014.00279; Kumar et al., 2012, Cancers (Basel);4:1252-1299; Qi et al., 2003, Nature Medicine; 9:407; and Yang et al.,2014, Biochim Biophys Acta; 1840:3483-3493). Transcripts important toblood pressure regulation as well as several genes linked toatherosclerosis were also identified as altered in PE C-RNA profiles,consistent with maternal vascular disease as an underlying mechanismpredisposing some patients to PE (Calò et al., 2014, J Hypertens;32:331-338; and Magnusson et al., 2012, PLOS ONE; 7:e43142). Thetranscripts identified captured a diversity of PE-relevant functions andhighlights the ability of C-RNA to simultaneously monitor the numerousmolecular processes implicated in complex disease.

Next, it was determined if C-RNA could not only detect biologicallyrelevant changes but also accurately classify pregnancies affected byearly-onset severe PE. The careful approach to AdaBoost model buildingdescribed herein identified combinations of transcripts that canclassify across distinct patient subsets, while excluding features thatcould lead to overfitting and bias in our model. Although 76% oftranscripts used by AdaBoost were not identified as differentiallyabundant, they still reflect the same PE-relevant pathways that werecaptured in our jackknifing analysis. The success of this strategy wasillustrated by the highly accurate classification of the independentearly-onset PEARL PEC. These samples were collected at the time ofdiagnosis from a different population than the one used for training,with less stringent inclusion and exclusion criteria. For example, thiscohort included 5 women who had chronic hypertension or gestationaldiabetes only 1 of which was misclassified by a few AdaBoost models,indicating the C-RNA changes utilized in machine learning were highlyspecific to PE.

This example provides an important step towards improved understandingand diagnosis of PE. The limited size of our clinical cohorts does notcapture the phenotypic diversity of the global pregnant population andthis is reflected in the lower values obtained for sensitivity thanspecificity in the classification analyses. Larger cohorts will betterencompass the heterogeneity of PE, identifying signals for diversemanifestations of disease. A second limitation is the targeted nature ofwhole transcriptome enrichment and as such, does not capture the fullrange of non-coding or non-human transcripts present in plasma. Certaininfections and miRNAs have been associated with PE (NourollahpourShiadeh et al., 2017, Infection; 45:589-600; and Skalis et al., 2019,Microrna; 8:28-35). Thus, future studies should aim to incorporate thesemeasurements with C-RNA transcriptomic data. While the findings of thisexample are highly consistent with what is reported in the literature,alterations in transcripts of widely reported serum protein biomarkerssuch as soluble FLT1 (sFLT), vascular endothelial growth factor (VEGF)or placental growth factor (PIGF) were not observed (Phipps et al.,2019, Nature Reviews Nephrology; 15:275). This result is unsurprisinggiven that gene expression is not always correlated with proteinabundance or release into the circulation.

The iPEC sample collection was focused specifically on early-onset PEwith severe features at the time of diagnosis. While this is the mostextreme phenotype of the disease, it represents a small percent of PEcases and further in-depth exploration across the clinical PE spectrumis warranted. As a preliminary examination, the AdaBoost model wasapplied to the late-onset PEARL PEC cohort and achieved reasonably goodaccuracy (72%). This is surprising, given the substantial evidence thatearly- and late-onset PE are distinct conditions, despite sharing afinal common phenotype of placental dysfunction (Burton et al., 2019,BMJ; 366:12381). Thus, some of the transcripts identified by AdaBoostlikely reflect the response to uteroplacental insufficiency rather thanits source and therefore may not have predictive value early in diseaseprogression. In contrast, alterations in transcripts involved inangiogenesis and trophoblast invasion (Xie et al., 2018, Res Commun;506:692-697; Hunkapiller et al., 2011, Development; 138:2987-2998; andChrzanowska-Wodnicka, 2017, Curr Opin Hematol; 24; 248:255), knownmolecular drivers of PE initiation, were observed, and may havepredictive value early in disease progression. Regardless of whether thechanges detected represent cause or effect, or even a combinationthereof, the methods and findings described herein show that C-RNAprovides a unique opportunity to build robust diagnostic algorithms andinvestigate mechanisms of disease that were never considered before.

The successful classification of PE patients at the time of diagnosisshowcases how C-RNA profiles can be used to robustly monitor maternal,fetal and placental functions in real-time. Future studies should focusearlier in pregnancy to evaluate the potential of this approach toimprove prognostication and prediction of outcomes for women with PE.Indeed, such studies hold great promise for uncovering predictivebiomarkers for early stratification of all at-risk pregnancies,informing prophylactic interventions or more vigilant monitoring of thepregnancy. The application of C-RNA will ultimately providecomprehensive molecular monitoring of maternal and fetal healththroughout the course of pregnancy.

Materials and Methods

Study Design

The objective of this example was to determine whether C-RNA can detectmolecular markers associated with early onset PE with severe features.This goal was achieved by (i) optimizing a protocol to obtain robustwhole transcriptome C-RNA measurements, (ii) analyzing blood plasmaC-RNA profiles from patients at the time of PE diagnosis andgestationally age-matched control pregnancies, and (iii) validating ourfindings with C-RNA data generated from an independent cohort. Theclinical protocol and informed consent forms for the iPEC study wereapproved by each clinical site's Institutional Review Board; inclusionand exclusion criteria are specified in Table 15. Investigators wereblinded to sample status through the bioinformatic processing ofsequencing data.

Clinical Sample Collection

iPEC. Pregnant patients were recruited in an Illumina sponsored clinicalstudy protocol (NCT02808494) in compliance with the InternationalConference on Harmonization for Good Clinical Practice. Participantswere recruited across 8 different clinical sites: University of TexasMedical Branch (Galveston, Tex.), Tufts Medical Center (Boston, Mass.),Columbia University Irving Medical Center (New York, N.Y.), WinthropUniversity Hospital (Mineola, N.Y.), St. Peter's University Hospital(New Brunswick, N.J.), Christiana Care (Newark, Del.), RutgersUniversity Robert Wood Johnson Medical School (New Brunswick, N.J.) andNew York Presbyterian/Queens (New York, N.Y.).

After obtaining informed consent, 20 mL whole blood samples werecollected from 40 singleton pregnancies with a diagnosis of PE before 34weeks' gestation with severe features defined per ACOG guidelines (Table15) (Hypertension in Pregnancy: Executive Summary, 2013, Obstet Gynecol;122:1122). Samples from 76 healthy pregnancies were also collected andwere matched for gestational age to the PE group. Three control samplesdeveloped term PE after blood collection and were excluded from thestudy. Maternal characteristics, birth outcomes (Table 32) andmedications (Table 16) in use were all recorded during the study.

PEARL. Plasma samples from the PEARL study (NCT02379832) were used as anindependent validation cohort. PEARL samples were collected at theCentre Hospitalier Universitaire de Québec (CHU de Québec). Onlyparticipants above 18 years of age were eligible, and all pregnancieswere singleton. A group of 45 control pregnancies (PEARL Healthy ControlCohort; PEARL HCC) and 45 case pregnancies (PEARL Preeclampsia Cohort;PEARL PEC) were recruited in this study and written informed consent wasobtained for all patients. A selection of plasma samples was obtainedafter the study had reached completion.

The criteria for PE was defined based on the Society of Obstetriciansand Gynecologists of Canada (SOGC) June 2014 criteria for PE, with agestational age requirement between 20 and 41 weeks, encompassing bothearly (diagnosed <34 weeks; N=12) and late onset (diagnosed >34 weeks,N=12) PE. A blood sample was taken once at the time of diagnosis fromthe PEARL PEC samples. The PEARL HCC included 45 pregnant women who wereexpected to have a normal pregnancy and were recruited between 11- and13-weeks' gestation. Each enrolled patient was followed longitudinallywith blood drawn at 4 timepoints throughout pregnancy until birth. Thecontrol women were divided into three subgroups and subsequent follow upblood draws were staggered to cover the entire range of gestational agesthroughout pregnancy (Table 13). In addition to using the PEARL HCCsamples to assess C-RNA in healthy pregnancy, samples from 24 uniqueindividuals in the PEARL HCC were selected to serve as gestationalage-matched controls for both the early- and late-onset PE cohort.

Sample Preparation

Plasma processing. All samples from the iPEC and the PEARL cohorts wereprocessed in randomized batches by investigators blinded to diseasestatus. Two tubes of blood were collected per patient in Cell-Free DNABCT tubes (Streck, Inc.). Blood samples collected in iPEC were storedand shipped at room temperature overnight and processed within 120hours. PEARL blood samples were collected, processed into plasma within24 hours and stored at −80° C. until shipped to Illumina on dry ice. Allblood was centrifuged at 1,600×g for 20 minutes at room temperature,plasma transferred to a new tube and centrifuged additional 10 minutesat 16,000×g. The plasma supernatant was stored at −80° C. until use.

Sequencing library preparation. C-RNA was extracted from 4.5 mL ofplasma with the Circulating Nucleic Acid Kit (Qiagen) followed by DNAseI digestion (Thermo Fisher Scientific) according to manufacturer'sinstructions. C-RNA was fragmented at 94° C. for 8 minutes followed byrandom hexamer primed cDNA synthesis using the Illumina TruSight Tumor170 Library Preparation kit (TST170; Illumina, Inc.). Illuminasequencing library preparation was carried out according to the TST170kit for RNA, with two modifications: all reactions were reduced to 25%of original volume, and the ligation adaptor was used at a 1 in 10dilution. Library quality was assessed with the High Sensitivity DNAAnalysis chips on the Agilent Bioanalyzer 2100 (Agilent Technologies).

Whole-transcriptome enrichment. Sequencing libraries were quantifiedwith Quant-iT PicoGreen dsDNA Kit (Thermo Fisher Scientific), normalizedto 200 ng input and 4 samples pooled per enrichment reaction. TheIllumina TruSeq RNA Enrichment kit (Illumina, Inc) was used to carry outwhole exome enrichment. Briefly, biotinylated oligos targeting the exomewere hybridized to sequencing libraries and pulled down by magneticsteptavidin beads to enrich libraries for exonic RNA. This process wasperformed two times to maximize exonic enrichment. Final enrichedlibrary was then re-amplified by PCR to provide sufficient yield forsequencing. Blocking oligos lacking the 5′ biotin designed againsthemoglobin genes HBA1, HBA2, and HBB were included in the enrichmentreaction to minimize contributions from highly abundant hemoglobin.Final enrichment libraries were quantified using Quant-IT PicogreendsDNA Kit (Thermo Fisher Scientific), normalized and pooled for pairedend 50 by 50 sequencing on Illumina HiSeq 2000 platforms to a minimumdepth of 40 million reads per sample.

Sequencing Data Analysis

Bioinformatic processing of sequencing data. Fastq files containing over50 million reads were downsampled to 50 million reads with seqtk(v1.2-r102-dirty). Sequencing reads were mapped to human referencegenome (hg19) with TopHat2 (v2.0.13) (Yang et al., 2014, Biochim BiophysActa; 1840:3483-3493), and transcript abundance quantified withfeatureCounts (subread-1.4.6) (Calò et al., 2014, J Hypertens;32:331-338) against RefGene coordinates (obtained Oct. 27, 2014). Tissueexpression data were obtained from Body Atlas (Correlation Engine, BaseSpace, Illumina, Inc) (Whittle et al., 2019, Front Microbiol;9:doi:10.3389/fmicb.2018.03266). Genes with expression ≥2-fold higherthan the median expression across all tissues in the placenta or any ofthe fetal tissues (brain, liver, lung, and thyroid) were assigned tothat group. Subcellular localization was obtained from UniProt(Magnusson et al., 2012, PLOS ONE; 7:e43142). Functional enrichmentanalyses were performed with gProfiler (v e97_eg44_p13 d22abce)(Raudvere et al., 2019, Nucleic Acids Res; 47:W191-W198).

Differential expression analysis. Differential expression analysis wasperformed in R (v3.4.2) with edgeR (v3.20.9), after excluding genes with≤0.5 counts per million reads sequenced (CPM) in >25% of samples.Datasets were normalized by the TMM method (Debieve et al., 2011, MolHum Reprod; 17:702-9), and differentially abundant genes identified bythe glmTreat test (Shiadeh et al., 2017, Infection; 45:589-600) for alog fold change ≥1, followed by Bonferroni-Holm p-value correction. Forthe iPEC data, this same process was used for each jackknifingiteration, which used 90% of samples in each group selected by randomsampling without replacement. After 1,000 jackknifing iterations, theone-sided, normal-based 95% confidence interval for gene-wise p-valueswas calculated with statsmodels (v0.8.0) (Skalis et al., 2019, Microrna;8:28-35). The one-sided calculation was used because only transcriptswith a p-value <0.05 were of interest. Hierarchical clustering analysiswas performed with squared Euclidean distance and average linkage.

AdaBoost. AdaBoost was performed in python with scikit-learn (v0.19.1,sklearmensemble.AdaBoostClassifier) (Burton et al., 2019, BMJ;366:12381). A 10% holdout subset of iPEC samples were excluded from allmachine learning activities. The remaining samples available fortraining machine learning were filtered to remove genes with a CPM ≤0.5in >25% of samples and TMM-normalized. The log(CPM) values oftranscripts were then standardized(sklearn.preprocessing.StandardScaler) to mean 0 and standard deviation1 prior to fitting classifiers.

Optimal hyperparameter values were determined by random search over1,000 iterations with 3-fold stratified cross-validation and usingMatthew's correlation coefficient to quantify performance (FIG. 42 )(Bergstra et al., 2012, J Machine Learning Res; 13:281-305). The numberof estimators was sampled from a geometric distribution(scipy.stats.geom, p=0.004, loc=7) while the learning rate was sampledfrom an exponential distribution (scipy.stats.expon, loc=0.08, scale=2).Three iterations of the search showed the highest performance, and themedian value for each hyperparameter was selected for further use (500estimators and 1.6 learning rate).

AdaBoost models were trained with 10-fold stratified cross-validation toobtain robust estimates of PE classification capabilities. The fullAdaBoost model training strategy is illustrated in FIG. 43 . Thisstrategy followed a two-step approach. In the first step, five subsetsof samples were created from the training data. Four subsets (80% oftraining data) were combined and used to fit AdaBoost and one subset(20% of the training data) was used to assess performance during featurepruning. During feature pruning, the performance measure was Matthew'scorrelation coefficient, and the model with the highest value wasretained. In the case of a tie, the model with the fewest transcriptswas retained. Due to the probabilistic nature of AdaBoost, modelcomposition varied each time a model was fit. Therefore, to increase thelikelihood that the most robust estimators were highly represented,fitting and feature pruning was repeated ten times, generating tenmodels for a subset. This process was repeated five times, in each roundholding out one subset for pruning and combining the four others forfitting. This step ultimately produced 50 total models.

In the second step, the estimators from all 50 models were combined togenerate a single aggregate AdaBoost model. This ensemble then underwentfeature pruning, in which the importance measure for each transcript wasthe number of models in which it appeared, and the performance measurewas log loss. The model with the best log-scaled average performanceacross all pruning subsets was selected as the final ensemble.

Within each fold, the validation samples were fully excluded from modeltraining, but were used to construct ROC curves and determine the scorethreshold which maximized both sensitivity and specificity. The statusof the iPEC holdout samples and the PEARL PEC independent cohorts werethen classified with each of the 10 AdaBoost models from thecross-validation.

RT-qPCR

25 TaqMan probes (Table 19; Thermo Fisher Scientific) were selected tovalidate sequencing results in a subset of patients from the iPEC cohort(N=19 PE, N=19 controls). 5 reference probes were used for normalizationof fold change differences. These targeted a set of transcriptsunchanged between control and PE samples and covered a range ofabundances from 0.2 to 20 CPM. Table S20 shows probes selected to spanexon junctions.

C-RNA was isolated and converted to cDNA from 2 mL of plasma. cDNA waspre-amplified using the TaqMan Preamp master Mix (Thermo FisherScientific) for 16 cycles, then diluted 10-fold. Triplicate TaqMan qPCRreactions were carried out for all probes per the manufacturer'sprotocol (Thermo Fisher Scientific). Cq values were determined usingBio-Rad CFX manager software. To determine transcript abundance, theΔΔCq was calculated using the mean Cq values of the reference probes. Todetermine the fold change in PE samples for each probe, the average ΔΔCqvalue for PE samples was divided by the average ΔΔCq value for thematched control samples.

Sample Preparation Protocol Optimization

rRNA and globin depletion. C-RNA was extracted from 2 mL plasma andDNAse treated prior to depletion. Use of the TruSeq Total RNA Librarykit with RiboZero (Illumina, Inc.) followed the manufacturer's protocol.RNAseH depletion followed previously published protocols (Crescitelli etal., 2013, J Extracell Vesicles; 2:doi:10.3402/jev.v2i0.20677), exceptfor hybridization which was performed in 6 uL total volume with a finalconcentration 125 pM/oligo for the depletion oligos.

C-RNA quantification. C-RNA was extracted from 4.5 mL plasma and DNAsetreated. One tenth of the extracted C-RNA was used for quantificationwith the Quant-iT RiboGreen RNA Kit (Thermo Fisher Scientific). C-RNAwas diluted 100-fold and quantified against the low range standard curveas recommended by the manufacture.

Plasma input comparison. Although no single experiment simultaneouslyassessed use of 0.5, 1, 2, and 4 mL plasma input, multiple datasetsutilizing different plasma inputs were generated during protocoloptimization. A meta-analysis was performed on the data from eightseparate experiments to assess the impact of this variable. Biologicalcoefficient of variation (BCV, edgeR) was used to quantify noise(Debieve et al., 2011, Mol Hum Reprod; 17:702-9). For every experiment,a BCV measurement was obtained for each set of samples composing abiologically distinct group. The bound population function from Preseqprovided library complexity estimates (v2.0.0) (Xie et al., 2018, ResCommun; 506:692-697) for each individual sample. All sample preparationwas performed as previously described with one exception: for 1 mL and0.5 mL inputs, the Accel-NGS 1S Plus DNA Library Kit (Swift Biosciences)was used to generate libraries, following manufacturer instructions.

BCT comparison. 8 mL blood was drawn from pregnant and non-pregnantwomen in the following tube types: K2 EDTA (Beckton Dickinson), ACD(Beckton Dickinson), Cell Free RNA BCT tube (Streck), and Cell Free DNABCT tube (Streck, Inc.). Blood was shipped overnight either on ice packs(EDTA and ACD) or at room temperature (Cell Free RNA and DNA BCT tubes).As a reference, 8 mL of blood was collected in K2 EDTA tubes andprocessed within 4 hours into plasma on site and shipped as plasma ondry ice. All other blood samples were processed after shipping after 1or 5 days of storage at the preferred temperature. 3 mL of plasma wasused per condition to generate sequencing libraries for enrichment usingIllumina protocols as described previously. To compare pregnant andnon-pregnant samples, pregnancy signal was quantified using 155transcripts reported in prior C-RNA pregnancy studies (Tsui et al.,2014, Clin Chem; 60:954-962; and Koh et al., 2014, Proc Natl Acad SciUSA; 111:7361-7366) with the following equation:

$\sum\limits_{i = 1}^{155}\frac{x_{i} - m_{i}}{s_{i}}$Where i denotes a single transcript, x is the log(CPM) value for thesample of interest, and m and s are the mean and standard deviation,respectively, for non-pregnant samples collected in the same BCT.

Pregnancy Timecourse analysis. Differential expression analysis wasperformed as described previously, without jackknifing. Transcriptsaltered during specific stages of gestation were identified as follows.The CPM values for each transcript were normalized within each patientusing the first trimester sample (11-14 weeks gestational age) asbaseline. The consensus values across all patients was obtained byLowess smoothing (statsmodels.nonparametric.smoothers_lowess.lowess). Atranscript was classified as altered earlier in pregnancy if the slopeof the Lowess curve was ≥2-fold higher in absolute magnitude at 14 weeksthan at 34 weeks gestational age; transcripts were categorized asaltered later in pregnancy if the slope of the Lowess curve was ≥2-foldhigher in absolute magnitude at 34 weeks than at 14 weeks gestationalage. The remaining transcripts were categorized as altered throughoutpregnancy.

Statistical Analysis

Unless otherwise noted, all statistical testing was two-sided.Non-parametric testing was used when data were not normally distributed.P-values were adjusted for multiple comparisons via Bonferroni-Holm orTukey HSD calculations.

TABLE 12 Study characteristics for the Illumina Preeclampsia Cohort(iPEC). Continuous measurements presented as mean +− SD. Sample andMaternal Characteristics Sample Sample Gestational Male MaternalMaternal Size Age Fetus Age BMI Nulliparous (N) (weeks) (%) (years)(kg/m²) (%) Control 73 30.5 ± 2.6 42.5 29.7 ± 5.3 30.1 ± 5.6 38.4 PE 4030.4 ± 2.6 37.5 30.4 ± 5.7 34.2 ± 5.8 32.5 Ethnicity/Race AfricanHispanic Caucasian American Asian Other Unknown (%) (%) (%) (%) (%) (%)Control 41.1 46.6 17.8 13.7 1.4 20.5 PE 35 35 27.5 7.5 0 30 BirthOutcomes Birth Gestational Preterm Birth Age Delivery Stillbirth WeightSGA* (weeks) (%) (%) (kg) (%) Control 38.9 ± 1.8 9.6 0 3.2 ± 0.6 9.6 PE31.5 ± 3.2 97.5 2.5 1.4 ± 0.5 45 *SGA, small for gestational age,defined as birthweight <10% of population for male or female neonate.

TABLE 13 PEARL HCC Gestational Age Distribution. 45 women with healthypregnancies were divided into three groups and blood collected at 4 timepoints. Patient Number of Collection 1 Collection 2 Collection 3Collection 4 Group Patients (weeks*) (weeks*) (weeks*) (weeks*) 1 1411^(0/7)-13^(6/7 †) 14^(0/7)-17^(6/7) 26^(0/7)-28^(6/7 ) 35^(0/7)-37^(6/7 †) 2 13 11^(0/7)-13^(6/7 †)   18^(0/7)-21^(6/7 †)29^(0/7)-31^(6/7 †) 35^(0/7)-37^(6/7 ‡) 3 14 11^(0/7)-13^(6/7 §)22^(0/7)-25^(6/7) 32^(0/7)-34^(6/7 †) 35^(0/7)-37^(6/7 †) *Indicated asa range from the minimum gestational age to the maximal gestational age,both in [weeks^(days/7)]. ^(†)1 sample failed library preparation ^(‡)2samples failed library preparation ^(§)3 samples failed librarypreparation

TABLE 14 PEARL HCC pregnancy progression transcripts. Timing of TissueGene Fold Alteration in Expression Symbol Change* Pregnancy** CategoryACOXL +27 Early Other ADAMI2 +5.9 Throughout Placental/Fetal AIM1L +8.1Throughout Placental AKR1B15 +12.7 Throughout Other ALDH3B2 +26 EarlyOther ALPP +64.5 Early Placental/Fetal ANK3 +3.5 Early Placental/FetalANKFN1 +12.1 Throughout Placental ANKRD33 +9.1 Throughout Placental AOC1+6.1 Throughout Other ATP6V1C2 +11.5 Early Placental BCAR4 +12.9Throughout Placental C2orf72 +20.6 Throughout Placental/Fetal C4orf19+8.9 Early Placental/Fetal CAMSAP3 +6.1 Late Fetal CAP2 +4.6 ThroughoutOther CAPN6 +26.4 Throughout Placental CDO1 +3.7 ThroughoutPlacental/Fetal CFB +3.6 Early Placental/Fetal CGB5 −17.0 Early OtherCGB8 −22.0 Early Other CRH +54.4 Late Placental/Fetal CRYAB +8.2 EarlyOther CSH1 +7.5 Throughout Placental/Fetal CSHL1 +48.4 EarlyPlacental/Fetal CYP11A1 +14.5 Early Placental CYP19A1 +7 ThroughoutPlacental/Fetal DACT2 +9.2 Throughout Placental DBET +9.9 ThroughoutOther DDX3Y +19.9 Early Other DEPDC1B +4 Early Placental/Fetal DLG5 +4.9Throughout Placental/Fetal DLX3 +9 Late Placental DUSP4 +3.7 ThroughoutPlacental/Fetal EFHD1 +6.7 Throughout Placental EFS +7 ThroughoutPlacental EGFR +4.3 Throughout Placental/Fetal ELF3 +11 Early FetalEPS8L1 +6.7 Throughout Placental/Fetal EPS8L2 +3.6 Throughout PlacentalERVV-1 +8.5 Throughout Other ERVV-2 +7.5 Early Other ESRP2 +11.5Throughout Other ESRRG +12.3 Early Placental EXPH5 +37.7 ThroughoutPlacental FBN2 +5.6 Throughout Placental/Fetal FER1L6 +8.3 Late OtherFOLR1 +7 Throughout Placental/Fetal FRZB +6 Early Placental FXYD3 +9.1Early Placental/Fetal GADD45G +5.6 Throughout Placental GCM1 +5.7Throughout Placental GDA +11.2 Early Placental/Fetal GLDN +8.2 EarlyPlacental GOLGA8K −932.4 Throughout Other GPC3 +6.1 Throughout FetalGRAMD2 +10.4 Throughout Placental GRB7 +11.6 Throughout Placental GRHL2+26.8 Early Placental GRIP1 +4.3 Early Placental/Fetal GSTA3 +32.1 EarlyPlacental HES2 +12.5 Early Placental HSD17B1 +9.5 Early Placental HSD3B1+12.7 Early Placental HSPB8 +4.6 Early Placental IL36RN +9.9 Early OtherKIAA1522 +3.2 Throughout Other KIF1A +18.7 Throughout Fetal KLF5 +3.4Throughout Other KRT18 +3.9 Early Placental/Fetal KRT19 +4.2 ThroughoutPlacental/Fetal KRT8 +4.4 Early Placental/Fetal KRT80 +10.3 ThroughoutOther KRT81 +13 Throughout Other LAD1 +8.6 Throughout Other LEP −46.2Throughout Placental LGALS13 +7.7 Early Placental/Fetal LGALS14 +12.2Early Placental LIN28B +7.2 Late Placental LINC00967 +22.5 EarlyPlacental LINC01118 +6.4 Early Placental LY6G6C +11.2 Throughout OtherMAGEA4 +10.4 Throughout Other MFSD2A +3.6 Throughout Placental MMP8 +4.8Early Fetal MOCOS +5.1 Early Other MORN3 +4 Throughout Placental MSX2+7.2 Throughout Placental MT1G −68.8 Early Fetal MUC15 +23.1 EarlyPlacental NCCRP1 +5.7 Throughout Placental NCMAP +25 Early Other NRK+13.3 Early Placental OLAH +7.1 Early Placental OVOL1 +13.2 ThroughoutFetal PACSIN3 +7.5 Throughout Other PAGE4 +1 1.5 Throughout PlacentalPAPPA +15.9 Early Placental PAPPA2 +7 Late Placental PCDH11X +7.3 EarlyOther PCDH11Y +20.3 Early Other PDZD2 +3.1 Throughout Placental/FetalPGF +9.5 Early Placental PHYHIPL +12.5 Early Placental/Fetal PKIB +5.6Early Placental PKP3 +10.5 Throughout Placental PLAC1 +32.7 EarlyPlacental/Fetal PLAC4 +6.9 Early Placental PLEKHG6 +10.5 ThroughoutOther PLEKHH1 +5.2 Early Placental POU2F3 +10.9 Throughout OtherPPP1R14C +10.9 Throughout Placental/Fetal PTPN3 +7.4 Early Other PVRL3+4.1 Early Placental/Fetal PVRL4 +7.8 Throughout Placental RAB25 +7.8Early Other RAB3B +43.7 Early Placental/Fetal RETN +4.1 Early Other RHOD+10.6 Early Placental/Fetal RLN2 −125.4 Late Other S100P +4.1 EarlyPlacental/Fetal SCIN +7.1 Throughout Placental SEMA3B +4 Early PlacentalSERPINB2 +13.4 Early Placental SLC27A6 +10.1 Throughout PlacentalSLC28A1 +10.4 Late Fetal SLC30A2 +7.5 Throughout Placental SLC6A2 +6.4Early Placental/Fetal SLC7A2 +6.2 Throughout Placental/Fetal SMOC2 +14.2Throughout Other SPIRE2 +18.3 Early Placental/Fetal SPTLC3 +5.6Throughout Placental STRA6 +7.5 Early Placental/Fetal SULT2B1 +35.4Early Other SVEP1 +12.5 Early Placental TACC2 +36 Throughout PlacentalTBX20 +9.4 Early Other TEAD3 +9.5 Early Placental TFAP2A +17.9 EarlyPlacental TFAP2C +9.4 Early Placental TGM2 +3.9 ThroughoutPlacental/Fetal TMEM54 +11.2 Early Placental TNS4 +12.7 ThroughoutPlacental TPPP3 +6.2 Throughout Placental/Fetal TPRXL +12.3 EarlyPlacental TRIM29 +18.5 Early Placental TRPV6 +16.7 Early PlacentalTWIST1 +19.7 Throughout Placental USP43 +11.1 Early Placental UTY +18.6Early Other VGLL1 +7.1 Early Placental/Fetal VGLL3 +13.6 Early PlacentalWWC1 +6.1 Early Fetal XAGE3 +11 Early Placental ZFY +16.3 Late OtherZNF750 +12.4 Early Placental *Reporting the maximal fold change observedbetween any pairwise comparison of age groups. All changes are relativeto the lower GA group-positive change indicates increased abundancelater in pregnancy. **If the slope of the log2(Fold Change) for atranscript is >2-fold higher at 14 weeks GA than at 34 weeks GA, it isconsidered altered early in pregnancy; if >2-fold higher at 34 weeksthan at 14 weeks, it is considered altered late in pregnancy; if bothfold changes are <2, it is considered altered throughout pregnancy.

TABLE 15 The iPEC diagnostic and inclusion/exclusion criteria for PEwith severe features. Diagnostic Criteria Measurement ManifestationBlood Pressure 1. Systolic BP ≥160 mmHG or diastolic BP ≥160 mmHgmeasured on at least 2 occasions 4 hours apart while on bedrest butbefore the onset of labor, or measured on 1 occasion only ifantihypertensive therapy is initiated due to severe hypertension Definedby one of the following: Proteinuria 1. Excretion of ≥300 mg of proteinin a 24 hr period 2. Protein/creatinine value of at least 0.3 3.Qualitative determination with urine dipstick of ≥1+, if the abovemeasurements were not available OR Blood Pressure 1. Systolic BP ≥140mmHg or diastolic ≥90 mmHg on at least 2 occasions 4 hours apart whileon bedrest but before the onset of labor With one of the 1.Thrombocytopenia (<100,000 platelets/mL) following features 2. Impairedliver function 3. Newly developed renal insufficiency 4. Pulmonary edema5. New-onset cerebral disturbances or scotomata Inclusion/ExclusionCriteria Category Requirements PE Inclusion 1. Women 18 years of age orolder 2. Pregnant women with a viable singleton gestation 3. Gestationalage between 20 0/7 and 33 6/7 weeks determined by ultrasound and/or LMPper ACOG guidelines 4. Preeclampsia diagnosed with severe features perACOG guidelines PE Exclusion 1. Known malignancy 2. History of maternalorgan or bone marrow transplant 3. Maternal blood transfusion in thelast 8 weeks 4. Chronic hypertension diagnosed prior to currentpregnancy 5. Type I, II or gestational diabetes 6. Fetal anomaly orknown chromosome abnormality Control Inclusion 1. Women 18 years of ageor older 2. Pregnant women with a viable singleton gestation 3.Gestational age between 20 0/7 and 33 6/7 weeks determined by ultrasoundand/or LMP per ACOG guidelines. Control Exclusion 1. Known malignancy 2.History of maternal organ or bone marrow transplant 3. Maternal bloodtransfusion in the last 8 weeks 4. Chronic hypertension diagnosed priorto current pregnancy 5. Type I, II or gestational diabetes 6. Fetalanomaly or known chromosome abnormality 7. Active labor 8.Thrombocytopenia (<100,000 plts/mL) 9. Impaired liver function 10. Newlydeveloped renal insufficiency (serum creatine >1.1 mg/dL) 11. Pulmonaryedema 12. New-onset cerebral disturbances or scotomata 13. Preeclampsiain prior or current pregnancy 14. Fetal growth restriction 15.

TABLE 16 Medications in use in the iPEC. Treatment PE Control PurposeMedication* Cohort (%) Cohort (%) PE/Hypertension Magnesium sulfate 82.54.1 Antenatal Steroids 95 6.8 Anti-Hypertensive 75 5.3 Aspirin 20 0Pregnancy Symptoms Antiemetics 25 5.5 Antacids 27.5 8.2Anti-constipation 15 11.8 Prenatal Vitamins 17.5 31.5 Iron Supplement 1012.3 Other Conditions Analgesics 60 11.8 Antimicrobials 12.5 5.5Antihistamines 32.5 13.7 Antiasthmatics 10 2.7 Psychoactive 15 5.5Hormone Therapy 7.5 2.7 *Medication category listed for most drugsrather that enumerating all specific pharmaceuticals.

TABLE 17 Medical center collection site patient distribution for theiPEC. Location PE patients Controls Clinical Site (city, state) (N) (N)University of Texas Medical Branch Galveston, Texas 4 11 Tufts MedicalCenter Boston, MA 10 17 Columbia University Irving Medical Center NewYork, NY 4 9 Winthrop University Hospital Mineola, NY 5 9 St. Peter'sUniversity Hospital New Brunswick, NJ 3 6 Christiana Care Newark, DE 713 Rutgers University Robert Wood Johnson Medical New Brunswick, NJ 5 8School New York Presbyterian/Queens New York, NY 2 3

TABLE 18 Study characteristics for PEARL PEC. Continuous measurementspresented as mean +− SD. Sample and Maternal Characteristics SampleSample Gestational Male Maternal Maternal Size Age Fetus Age BMINulliparous (N) (weeks) (%) (years) (kg/m²) (%) Early- 12 29.3 ± 2.358.3 30.1 ± 3.8 28.5 ± 7  58.3 Onset Control Early- 12 29.2 ± 2.3 7529.3 ± 3.5 33.6 ± 9  60 Onset PE Late- 12 35.9 ± 0.8 58.3 29.4 ± 3.227.9 ± 4.5 75 Onset Control Late- 12 35.6 ± 1.3 66.7 30.2 ± 4.8 32.2 ±4.9 75 Onset PE Ethnicity/Race African Hispanic Caucasian American AsianOther Unknown (%) (%) (%) (%) (%) (%) Early- 0 100 0 0 0 0 Onset ControlEarly- 0 91.7 8.3 0 0 0 Onset PE Late- 0 100 0 0 0 0 Onset Control Late-0 100 0 0 0 0 Onset PE Birth Outcomes Birth Gestational Term PretermBirth Age Delivery Delivery Stillbirth Weight SGA* (weeks) (%) (%) (%)(kg) (%) Early- 39.1 ± 1.5 91.7 8.3 0 3.2 ± 0.4 0 Onset Control Early-30.3 ± 3.4 0 100 0 1.3 ± 0.5 25 Onset PE Late- 39.7 ± 1.6 91.7 8.3 0 3.4± 0.5 25 Onset Control Late-  37 ± 1.4 75 25 0 2.7 ± 0.6 33.3 Onset PEAdditional Health Issues Chronic Type I, II Gestational Fetal GrowthHypertension Diabetes Diabetes Restriction HELLP Other Early- 8.3 0 33.30 0 8.3 Onset Control Early- 16.7 16.7 16.7 50 25 0 Onset PE Late- 0 016.7 0 0 0 Onset Control Late- 8.3 25 8.3 8.3 0 0 Onset PEPE/Hypertension Medications Magnesium Antenatal Anti- Sulfate SteroidsHypertensive Aspirin Early- 0 0 8.3 25 Onset Control Early- 83.3 100 1008.3 Onset PE Late- 0 0 0 8.3 Onset Control Late- 33.3 25 75 25 Onset PE*SGA, small for gestational age, defined as birthweight <10% ofpopulation for male or female neonate.

TABLE 19 TaqMan Probes for qPCR validation. Gene Control CPM* PE CPM*Symbol Assay ID RefSeq (mean ± SD) (mean ± SD) Reference Probes ABHD12Hs01018050_m1 NM_001042472.2 20.7 ± 6.5  20.5 ± 4.7  KRBOX4Hs01063506_gH NM_001129898.1 5.1 ± 2  5.4 ± 1.8 NME3 Hs01573872_g1NM_002513.2 1.6 ± 0.8 1.8 ± 0.8 WNT7A Hs00171699_m1 NM_004625.3  0.3 ±0.03 0.3 ± 0.1 ZNF138 Hs00864088_gH NM_001271638.1 8.1 ± 3.3 7.5 ± 3.2Target Probes ADAMTS2 Hs01029111_m1 NM_014244.4 0.6 ± 1.2 7.8 ± 7.5ALOX15B Hs00153988_m1 NM_001039130.1 0.3 ± 0.3 1.9 +± .1  ARHGEF25Hs00384780_g1 NM_001111270.2 1.3 ± 1  5.5 ± 2.6 CLEC4C Hs01092460_m1NM_130441.2 4.3 ± 2.3 1.3 ± 0.9 DAAM2 Hs00322497_m1 NM_001201427.1 1.6 ±1.8 9.6 ± 9.6 FAM107A Hs00200376_m1 NM_001076778.2 8.3 ± 5.5 44.2 ± 35.4HSPA12B Hs00369554_m1 NM_001197327.1 5.9 ± 3.5 22.4 ± 11.7 HTRA4Hs00538137_m1 NM_153692.3 0.4 ± 0.4 1.6 ± 1.2 IGFBP5 Hs00181213_m1NM_000599.3 10.3 ± 5.8  39.9 ± 26.1 KRT5 Hs00361185_m1 NM_000424.3 3.1 ±3.3 0.6 ± 0.7 LEP Hs00174877_m1 NM_000230.2 0.2 ± 0.5 1.8 ± 1.5 NESHs00707120_s1 NM_006617.1 20.8 ± 12.5 101.6 ± 61   PAPPA2 Hs01060983_m1NM_020318.2 5.9 ± 9.3 26.1 ± 16.6 PITPNM3 Hs01107787_m1 NM_001165966.122.6 ± 12.9 76.5 ± 43.6 PLD4 Hs00975488_m1 NM_001308174.1 5.5 ± 2.6  2 ±1.3 PRG2 Hs00794928_m1 NM_001243245.2 0.6 ± 0.5 3.3 ± 5.6 TIMP3Hs00165949_m1 NM_000362.4 3.7 ± 2.3 15.8 ± 10.7 TIMP4 Hs00162784_m1NM_003256.3 0.4 ± 0.4  2 ± 1.9 VSIG4 Hs00907325_m1 NM_001184830.1 3.6 ±3.8 30.9 ± 32.9 ZEB1 Hs01566408_m1 NM_001128128.2 245.9 ± 108.7 752.6 ±434  *Calculated from the iPEC sequencing data

TABLE 20 Transcripts with C-RNA abundances altered in early-onset PEwith severe features. Delineates the protein expression, functionalcharacteristics, and PE-relevant literature of the genes identified bydifferential expression analysis with jackknifing (DEX) from the iPECsamples. Changes in PE PMID of Fold Manuscripts Gene Change Describing aSymbol Analysis in PE Role in PE ALOX15B DEX +5.7 22078795 AMPH DEX +5.0NA CUX2 DEX & AdaBoost −3.3 NA FAM107A DEX +5.0 NA IGFBP5 DEX +3.628049695 NES DEX & AdaBoost +4.5 17653873 PITPNM3 DEX +3.2 NA PRX DEX+3.8 24657793 TEAD4 DEX & AdaBoost +3.3 NA PNMT DEX +3.8 NA DAAM2 DEX+5.6 20934677 SLC9A3R2 DEX +3.6 NA HSPA12B DEX +3.5 NA PLD4 DEX &AdaBoost −3.0 NA TIMP4 DEX +4.3 29231756 KRT5 DEX & AdaBoost −5.824657793 ZEB1 DEX +2.8 30315928 APOLD1 DEX +3.4 22013081 HTRA4 DEX +3.925946029 SEMA3G DEX & AdaBoost +3.5 NA ADAMTS1 DEX +3.5 29135310 CRH DEX+5.7 12709362 PRG2 DEX & AdaBoost +5.2 28347715 TIMP3 DEX +4.1 30715128ARHGEF25 DEX & AdaBoost +4.1 NA CLEC4C DEX & AdaBoost −3.6 12699426 LEPDEX & AdaBoost +10.7 23544093 PAPPA2 DEX +4.9 26748159 VSIG4 DEX &AdaBoost +8.1 24349325 ADAMTS2 DEX & AdaBoost +11.6 NA ProteinCharacteristics Gene Tissue Sub-Cellular Symbol ExpressionLocalization * Function ALOX15B Fetal Nucleus; Cell Cycle; ImmuneFunction; Cytoskeleton; Cardiovascular Function Cytosol; AMPH FetalMembrane Synaptic Vesicle Endocytosis Cytoskeleton; Membrane CUX2 FetalNucleus Cell Cycle; Fetal Development; DNA Damage Response FAM107A FetalCytoskeleton; Cell Migration/Invasion; Cell Membrane; Cycle; ECMRegulation Nucleus IGFBP5 Fetal Extracellular or Fetal Development; IGFSecreted Signaling NES Fetal Cytoskeleton Fetal Development; Cell CyclePITPNM3 Fetal Membrane Phosphatidylinositol Regulation PRX FetalMembrane Cell Structure/Composition TEAD4 Fetal Nucleus PlacentalDevelopment Epinephrine Synthesis; PNMT Other Cytosol CardiovascularFunction; Pregnancy Duration DAAM2 Other Extracellular or FetalDevelopment Secreted SLC9A3R2 Other Membrane; ECM Regulation; CellNucleus Structure/Composition Angiogenesis; Cardiovascular HSPA12B Otherunknown Function; Cell Migration/Invasion; Hypoxia Response PLD4 OtherMembrane Phosphatidylinositol Regulation; Immune Function TIMP4 OtherExtracellular or ECM Regulation; Immune Secreted Function KRT5 OtherCytoskeleton Cell Structure/Composition Immune Function; Cell ZEB1 OtherNucleus Migration/Invasion; Fetal Development; Pregnancy Duration APOLD1Placental Plasma Angiogenesis; Cardiovascular Membrane Function; HypoxiaResponse; Fetal Development HTRA4 Placental Extracellular or IGFSignaling; Placental Secreted Development SEMA3G Placental Extracellularor Cell Migration/Invasion Secreted ADAMTS1 Placental/FetalExtracellular or ECM Regulation; Fetal Secreted Development;Angiogenesis CRH Placental/Fetal Extracellular or Pregnancy Duration;Fetal Secreted Development; Cardiovascular Function PRG2 Placental/FetalExtracellular or Immune Function; ECM Secreted Regulation; IGF SignalingTIMP3 Placental/Fetal Extracellular or ECM Regulation; Immune SecretedFunction; Angiogenesis ARHGEF25 Other Membrane; Cardiovascular functionSarcomere CLEC4C Other Membrane Immune Function LEP PlacentalExtracellular or Energy Homeostasis; Immune Secreted Function;Angiogenesis; Fetal Development; ECM Regulation PAPPA2 PlacentalExtracellular or Fetal Development; IGF Secreted Signaling VSIG4Placental Membrane Immune Function ADAMTS2 Placental/Fetal Extracellularor ECM Regulation; Secreted Angiogenesis; Fetal Development * All“membrane” classifications were collapsed to a single category.

TABLE 21 Transcripts used by AdaBoost for classification of early-onsetPE with severe features. Delineates the protein expression, functionalcharacteristics, and PE-relevant literature of the genes identified withthe iPEC model samples. Changes in PE PMID of Manuscripts Average FoldDescribing Gene Number of Relative Change a Role Symbol Analysis ModelsImportance* in PE in PE ADA AdaBoost 3 0% −1.44 27939490 ADAMTS2 DEX &AdaBoost 6 18%  +11.57 NA AKAP2 AdaBoost 1 1% +1.95 NA ARHGEF25 DEX &AdaBoost 10 25%  +4.09 NA ARRB1 AdaBoost 1 19%  −1.17 30503206 ARRDC2AdaBoost 8 7% +1.78 NA ATOH8 AdaBoost 5 2% +2.22 30301918 CLEC4C DEX &AdaBoost 8 8% −3.64 12699426 CPSF7 AdaBoost 1 1% +1.2 NA CUX2 DEX &AdaBoost 3 1% −3.3 NA FKBP5 AdaBoost 1 0% +2.8 26268791 FSTL3 AdaBoost 21% +2.57 30454705 GSTA3 AdaBoost 4 1% −2.54 28232601 HEG1 AdaBoost 3 3%+2.06 NA IGIP AdaBoost 4 1% +2.49 25802182 INO80C AdaBoost 2 2% −1.13 NAJAG1 AdaBoost 3 2% +2.13 21693515 JUN AdaBoost 6 3% +1.75 20800894 KRT5DEX & AdaBoost 2 3% −5.78 24657793 LEP DEX & AdaBoost 9 8% +10.7523544093 LILRA4 AdaBoost 4 2% −2.57 19368561 MRPS35 AdaBoost 1 0% −1.18NA MSMP AdaBoost 7 9% +1.37 29059175 NES DEX & AdaBoost 6 9% +4.517653873 NFE2L1 AdaBoost 3 7% +2.39 26089598 NR4A2 AdaBoost 1 1% +1.4218533121 NTRK2 AdaBoost 1 1% +2.67 21537405 PACSIN1 AdaBoost 1 1% −3.22NA PER1 AdaBoost 3 5% +2.33 NA PLD4 DEX & AdaBoost 4 2% −3.03 NA PLEKAdaBoost 1 19%  −1.37 NA PRG2 DEX & AdaBoost 1 1% +5.24 28347715RAP1GAP2 AdaBoost 6 2% −1.23 29643944 RGP1 AdaBoost 3 1% +1.26 NA SEMA3GDEX & AdaBoost 1 0% +3.48 NA SH3PXD2A AdaBoost 2 4% +2.36 23544093 SKILAdaBoost 7 7% +1.46 20934677 SMPD3 AdaBoost 1 0% −2.3 23465879 SPEGAdaBoost 3 2% +1.87 NA SRPX AdaBoost 1 1% +2.89 20934677 SYNPO AdaBoost1 1% +2.67 17255128 TEAD4 DEX & AdaBoost 3 5% +3.3 NA TIPARP AdaBoost 10% +1.28 28347715 TNFRSF21 AdaBoost 4 1% −1.52 NA TPST1 AdaBoost 1 0%+1.69 NA TRPS1 AdaBoost 1 0% +1.18 NA UBE2Q1 AdaBoost 1 50%  −1.1 NAVSIG4 DEX & AdaBoost 3 3% +8.13 24349325 ZNF768 AdaBoost 2 7% +1.6 NAProtein Characteristics Gene Tissue Sub-Cellular Symbol ExpressionLocalization Function ADA Other Lysosome; Membrane Metabolism;Inflammation ADAMTS2 Placental/Fetal Extracellular or ECM regulation;Angiogenesis; Fetal Secreted Development AKAP2 Other MembraneCardiovascular Function ARHGEF25 Other Membrane; Cardiovascular FunctionSarcomere ARRB1 Other Cytosol GPCR Signaling; Cardiovascular Function;Immune Function ARRDC2 Other Membrane Protein Trafficking ATOH8 OtherNucleus Transcription Factor; Pregnancy Duration; Trophoblast RegulationCLEC4C Other Membrane Immune Function CPSF7 Other Nucleus; CytoplasmmRNA Processing CUX2 Fetal Nucleus Cell Cycle; Fetal Development; DNADamage Response FKBP5 Other Nucleus; Cytoplasm Immune Function; SteroidHormone Receptor Trafficking FSTL3 Placental Extracellular or FetalDevelopment; Trophoblast Secreted; Nucleus Regulation GSTA3 PlacentalCytoplasm Steroid Hormone Biosynthesis; Placental Development HEG1 OtherMembrane; Fetal Development Extracellular or Secreted IGIP OtherExtracellular or Cardiovascular Function Secreted INO80C PlacentalNucleus Transcription Regulation; DNA Repair JAG1 Other Membrane FetalDevelopment; Angiogenesis; Trophoblast Regulation JUN Other NucleusTranscription Factor; Trophoblast Regulation KRT5 Other CytoskeletonCell Structure/Composition LEP Placental Extracellular or EnergyHomeostasis; Immune Function; Secreted Angiogenesis; Fetal Development;ECM Regulation LILRA4 Other Membrane Immune Function MRPS35 OtherMitochondria Energy Homeostasis; Cell Structure/Composition MSMP OtherExtracellular or Angiogenesis Secreted NES Fetal Cytoskeleton FetalDevelopment; Cell Cycle Cardiovascular Function; Oxidative NFE2L1 OtherMembrane; Nucleus Stress Response; Energy Homeostasis; TranscriptionFactor NR4A2 Other Nucleus; Cytoplasm Fetal Development; Steroid HormoneResponse; Transcription Factor NTRK2 Fetal Membrane TrophoblastRegulation; Fetal Development PACSIN1 Fetal Membrane CellStructure/Composition; Synaptic Vesicle Endocytosis PER1 Fetal Nucleus;Cytoplasm Circadian Rhythm PLD4 Other Membrane PhosphatidylinositolRegulation; Immune Function PLEK Fetal Cytosol PhosphatidylinositolRegulation; Immune Function; Cell Structure/Composition PRG2Placental/Fetal Extracellular or Immune Function; ECM Regulation; IGFSecreted Signaling RAP1GAP2 Placental/Fetal Cytoplasm Immune Function;Angiogenesis; Fetal Development RGP1 Other Cytosol; Membrane ProteinTrafficking SEMA3G Placenta Extracellular or Cell Migration/InvasionSecreted SH3PXD2A Other Cytoplasm ECM Regulation; Fetal Development SKILPlacenta Nucleus Transcription Factor; Placenta Development SMPD3 FetalMembrane Lipid Metabolism SPEG Other Nucleus Fetal Development SRPXOther Extracellular or Angiogenesis Secreted SYNPO Placenta Cytskeleton;Cytosol Cell Structure/Composition TEAD4 Fetal Nucleus PlacentalDevelopment TIPARP Other Nucleus Metabolism; Protein Processing TNFRSF21Other Membrane Immune Function; Apoptosis TPST1 Other Membrane ProteinProcessing TRPS1 Other Nucleus Transcription Factor UBE2Q1 OtherNucleus; Cytosol Protein Processing VSIG4 Placenta Membrane ImmuneFunction ZNF768 Other Nucleus Transcription Factor *Average for whenincluded in an AdaBoost model.

The complete disclosure of all patents, patent applications, andpublications, and electronically available material (including, forinstance, nucleotide sequence submissions in, e.g., GenBank and RefSeq,and amino acid sequence submissions in, e.g., SwissProt, PIR, PRF, PDB,and translations from annotated coding regions in GenBank and RefSeq)cited herein are incorporated by reference in their entirety.Supplementary materials referenced in publications (such assupplementary tables, supplementary figures, supplementary materials andmethods, and/or supplementary experimental data) are likewiseincorporated by reference in their entirety. In the event that anyinconsistency exists between the disclosure of the present applicationand the disclosure(s) of any document incorporated herein by reference,the disclosure of the present application shall govern. The foregoingdetailed description and examples have been given for clarity ofunderstanding only. No unnecessary limitations are to be understoodtherefrom. The disclosure is not limited to the exact details shown anddescribed, for variations obvious to one skilled in the art will beincluded within the disclosure defined by the claims.

Unless otherwise indicated, all numbers expressing quantities ofcomponents, molecular weights, and so forth used in the specificationand claims are to be understood as being modified in all instances bythe term “about.” Accordingly, unless otherwise indicated to thecontrary, the numerical parameters set forth in the specification andclaims are approximations that may vary depending upon the desiredproperties sought to be obtained by the present disclosure. At the veryleast, and not as an attempt to limit the doctrine of equivalents to thescope of the claims, each numerical parameter should at least beconstrued in light of the number of reported significant digits and byapplying ordinary rounding techniques.

Notwithstanding that the numerical ranges and parameters setting forththe broad scope of the disclosure are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspossible. All numerical values, however, inherently contain a rangenecessarily resulting from the standard deviation found in theirrespective testing measurements.

All headings are for the convenience of the reader and should not beused to limit the meaning of the text that follows the heading, unlessso specified.

What is claimed is:
 1. A method of detecting and treating preeclampsiain a subject pregnant human female, the method comprising: detecting ina blood, plasma, or serum sample from the subject pregnant human femalea level of circulating RNA (C-RNA) molecules encoding at least a portionof the protein UBE2Q1 protein, wherein the detected level is decreasedby about 1.1 fold relative to a level in a gestationally age matchedcontrol pregnant human female that does not have preeclampsia, andwherein the detected level indicates the presence of preeclampsia in thesubject pregnant human female; and providing the subject pregnant humanfemale with: a therapeutic intervention for the treatment ofpreeclampsia selected from the group consisting of antihypertensivemedications to lower blood pressure, corticosteroid medications,anticonvulsant medications, bed rest, early delivery, and combinationsthereof, and/or treating the subject pregnant human female with a lowdose of aspirin, wherein a low dose of aspirin comprises about 50 toabout 150 mg per day.
 2. A method of detecting and treating preeclampsiain a subject pregnant human female, the method comprising: obtaining abiosample from the subject pregnant human female; wherein the biosamplecomprises blood, plasma, or serum; purifying a population of circulatingRNA (C-RNA) molecules from the biosample; identifying protein codingsequences encoded by the C-RNA molecules within the purified populationof C-RNA molecules; detecting in the blood, plasma, or serum sample fromthe subject pregnant human female a level of C-RNA molecules encoding atleast a portion of the protein UBE2Q1 protein, wherein the detectedlevel is decreased by about 1.1 fold relative to a level in agestationally age matched control pregnant human female that does nothave preeclampsia, and wherein the detected level indicates the presenceof preeclampsia in the subject pregnant human female; and providing thesubject pregnant human female with a therapeutic intervention for thetreatment of preeclampsia selected from the group consisting ofantihypertensive medications to lower blood pressure, corticosteroidmedications, anticonvulsant medications, bed rest, early delivery, andcombinations thereof, and/or treating the subject pregnant human femalewith a low dose of aspirin, wherein a low dose of aspirin comprisesabout 50 to about 150 mg per day.
 3. The method of claim 1, whereindetecting the level of C-RNA molecules encoding at least a portion ofthe protein UBE2Q1 protein comprises hybridization, reversetranscriptase PCR, microarray chip analysis, or sequencing.
 4. Themethod of claim 3, wherein sequencing comprises massively parallelsequencing of clonally amplified molecules.
 5. The method of claim 3,wherein sequencing comprises RNA sequencing.
 6. A method of detectingand treating preeclampsia in a subject pregnant human female, the methodcomprising: removing intact cells from a biosample obtained from thesubject pregnant human female; wherein the biosample comprises blood,plasma, or serum; treating the biosample with a deoxynuclease (DNase) toremove cell free DNA (cfDNA); synthesizing complementary DNA (cDNA) fromcirculating RNA (C-RNA) molecules in the biosample; enriching the cDNAsequences for DNA sequences that encode proteins (exome enrichment);sequencing the resulting enriched cDNA sequences; and identifyingprotein coding sequences encoded by enriched C-RNA molecules; detectingin the blood, plasma, or serum sample from the subject pregnant humanfemale a level of C-RNA molecules encoding at least a portion of theprotein UBE2Q1 protein, wherein the detected level is decreased by about1.1 fold relative to a level in a gestationally age matched controlpregnant human female that does not have preeclampsia, and wherein thedetected level indicates the presence of preeclampsia in the subjectpregnant human female; and providing the subject pregnant human femalewith a therapeutic intervention for the treatment of preeclampsiaselected from the group consisting of antihypertensive medications tolower blood pressure, corticosteroid medications, anticonvulsantmedications, bed rest, early delivery, and combinations thereof, and/ortreating the subject pregnant human female with a low dose of aspirin,wherein a low dose of aspirin comprises about 50 to about 150 mg perday.
 7. The method of claim 1, wherein the blood, plasma, or serumsample is obtained from the subject pregnant human female at less than16 weeks gestation or at less than 20 weeks gestation.
 8. The method ofclaim 1, wherein the blood, plasma, or serum sample is obtained from thesubject pregnant human female at greater than 20 weeks gestation.
 9. Themethod of claim 1, wherein the blood, plasma, or serum sample is a bloodsample and the blood sample is collected, shipped, and/or stored in atube that has cell- and DNA-stabilizing properties prior to processingthe blood sample into plasma.
 10. The method of claim 1, wherein thebiosample blood, plasma, or serum sample is: a blood sample; not exposedto EDTA prior to processing the blood sample into plasma; processed intoplasma within about 24 to about 72 hours of the blood draw; maintained,stored, and/or shipped at room temperature prior to processing intoplasma; and/or maintained, stored, and/or shipped without exposure tochilling or freezing prior to processing into plasma.
 11. The method ofclaim 1, further comprising identifying one or more protein codingsequences encoded by the enriched C-RNA molecules comprising one or moreof AKAP2, ARRB1, CPSF7, INO80C, JAG1, MSMP, NR4A2, PLEK, RAP1GAP2, SPEG,TRPS1, and ZNF768.
 12. The method of claim 2, wherein detecting thelevel of C-RNA molecules encoding at least a portion of the proteinUBE2Q1 protein comprises hybridization, reverse transcriptase PCR,microarray chip analysis, or sequencing.
 13. The method of claim 2,wherein the blood, plasma, or serum sample is obtained from the subjectpregnant human female at less than 16 weeks gestation or at less than 20weeks gestation.
 14. The method of claim 2, wherein the blood, plasma,or serum sample is obtained from the subject pregnant human female atgreater than 20 weeks gestation.
 15. The method of claim 6, wherein theblood, plasma, or serum sample is obtained from the subject pregnanthuman female at less than 16 weeks gestation or at less than 20 weeksgestation.
 16. The method of claim 6, wherein the blood, plasma, orserum sample is obtained from the subject pregnant human female atgreater than 20 weeks gestation.
 17. The method of claim 6, whereinsequencing comprises massively parallel sequencing of clonally amplifiedmolecules.
 18. The method of claim 2, wherein the blood, plasma, orserum sample is: a blood sample; not exposed to EDTA prior to processingthe blood sample into plasma; processed into plasma within about 24 toabout 72 hours of the blood draw; maintained, stored, and/or shipped atroom temperature prior to processing into plasma; and/or maintained,stored, and/or shipped without exposure to chilling or freezing prior toprocessing into plasma.